This article studies the global asymptotic neural network (NN) tracking problem for full-state error constrained spacecraft attitude systems with actuator faults, inertia uncertainties, and external disturbances. In the literature, most existing NN control schemes can only achieve semiglobally bounded stability since the approximation capability of NNs is confined to a compact domain called the approximation domain. Differently, an attitude tracking control strategy in conjunction with a modified smooth switching mechanism is proposed to ensure the global asymptotic stability. Specifically, an adaptive NN controller is developed within the approximation domain to address unknown nonlinearities, and a robust controller is activated outside the approximation domain to drive back the system states. With the proposed design, both attitude and angular velocity errors (collectively defined as the full-state errors) are rigorously proven to globally asymptotically converge to zero. Moreover, the full-state errors are preserved within the unified prescribed performance constraints, which are uniform with respect to any initial conditions, thereby eliminating the requirement for offline computation of the performance boundary. In addition, the undesirable feasibility conditions on virtual control laws are completely eliminated. Theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

Abstract Solving combined economic emission dispatch (CEED) problem optimizes power generation by balancing cost minimization with emission reduction, addressing economic and environmental goals simultaneously. This trade-off results in a Pareto front, where each non-dominated solution represents an optimal balance between costs and emissions. Decision-makers can select solutions based on their preferences. This paper proposes an Epsilon-based multi-objective genetic algorithm (MOGA) to solve the CEED problem. By integrating evolutionary techniques and Epsilon constraint methods, the proposed method actively explores diverse solutions, avoiding local optima and enhancing the Pareto front. The two-objective CEED problem is reformulated into two single-objective problems, alternately minimizing cost or emissions. The Epsilon constraint algorithm accelerates the search for optimal solutions. Two quality indicators are proposed to evaluate the Pareto fronts. The first indicator measures solution spread; it assesses the diversity of generator settings and the dominated volume. The second indicator evaluates the uniformity of solution distribution, where smaller distances indicate better uniformity. High spread and uniform distribution signify superior Pareto fronts. If diversity is insufficient, the proposed Epsilon-based MOGA continues to refine the front. The performance of the proposed method was tested on IEEE 30-bus and 118-bus systems, showing improved results compared to RNSGA-II, the Epsilon constraint algorithm, and NSGA-II. The proposed method produced more diverse and uniformly distributed non-dominated solutions; it offers grid operators a broader range of options to balance costs and emissions. Additionally, it achieved lower costs and emissions. The computational time for the larger IEEE 118 system is manageable and does not increase exponentially compared to the smaller IEEE 30 system.

This article presents an adaptive neural network-based sliding mode control (SMC) strategy aimed at achieving predefined-time and predefined-accuracy (PTPA) convergence of tracking errors. Notably, the proposed approach does not require prior knowledge of the upper bounds of uncertainties or the control direction. To ensure PTPA convergence and prevent singularity, a novel piecewise PTPA sliding mode manifold incorporating a nonlinear compensating term is introduced. This compensating term is specifically designed to address the bounded sliding-mode errors induced by model uncertainties and external disturbances. Furthermore, by enforcing a PTPA constraint on the sliding mode function and utilizing the Nussbaum function, the system states can converge to a predefined neighborhood of the sliding mode surface within a predefined time. An adaptive neural network-based PTPA SMC law is then developed, eliminating discontinuous terms and effectively mitigating the chattering issue. The proposed control scheme is rigorously proven to achieve PTPA convergence and asymptotic convergence. The efficacy of the designed control strategy is validated through two numerical examples, demonstrating its superior performance.

This paper focuses on the cardinality constrained mean-variance portfolio optimization, in which only a small number of assets are invested. We first treat the covariance matrix of asset returns as a diagonal matrix with a special matrix processing technique. Using the dual theory, we formulate the lower bound problem of the original problem as a max-min optimization. For the inner minimization problem with the cardinality constraint, we obtain its analytical solution for the portfolio weights. Then, the lower bound problem turns out to be a simple concave optimization with respect to the Lagrangian multipliers. Thus, the interval split method and the supergradient method are developed to solve it. Based on the precise lower bound, the depth-first branch and bound method are designed to find the global optimal investment selection strategy. Compared with other lower bounds and the current popular mixed integer programming solvers, such as CPLEX and SCIP, the numerical experiments show that our method has a high searching efficiency. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the National Natural Science Foundation of China [Grants 12101317, 12271071, and 11991024], the Natural Science Foundation of Jiangsu Province [Grant BK20200819], the Team Project of Innovation Leading Talent in Chongqing [Grant CQYC20210309536], the Contract System Project of Chongqing Talent Plan [Grant cstc2022ycjh-bgzxm0147], and the Philosophy and Social Science Fund of Education Department of Jiangsu Province [Grant 2020SJA0168]. K.F.C. Yiu is supported in part by the Research Grants Council of Hong Hong [Grant PolyU 15223419], the Hong Kong Polytechnic University [Grants 4-ZZPT and 1-WZ0E], and the Research Centre for Quantitative Finance [Grant 1-CE03]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0344 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0344 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Abstract Deep neural networks (DNNs) have been applied in many pattern recognition or object detection applications. DNNs generally consist of millions or even billions of parameters. These demanding computational storage and requirements impede deployments of DNNs in resource-limited devices, such as mobile devices, micro-controllers. Simplification techniques such as pruning have commonly been used to slim DNN sizes. Pruning approaches generally quantify the importance of each component such as network weight. Weight values or weight gradients in training are commonly used as the importance metric. Small weights are pruned and large weights are kept. However, small weights are possible to be connected with significant weights which have impact to DNN outputs. DNN accuracy can be degraded significantly after the pruning process. This paper proposes a roulette wheel-like pruning algorithm, in order to simplify a trained DNN while keeping the DNN accuracy. The proposed algorithm generates a branch of pruned DNNs which are generated by a roulette wheel operator. Similar to the roulette wheel selection in genetic algorithms, small weights are more likely to be pruned but they can be kept; large weights are more likely to be kept but they can be pruned. The slimmest DNN with the best accuracy is selected from the branch. The performance of the proposed pruning algorithm is evaluated by two deterministic datasets and four non-deterministic datasets. Experimental results show that the proposed pruning algorithm generates simpler DNNs while DNN accuracy can be kept, compared to several existing pruning approaches.

In multimedia applications, it is common to employ acoustic sensors collectively to enhance signals and to locate sound sources. A direct problem can be formulated to locate sound sources from a set of known sensors. In order to form the acoustic sensor network, it is important to locate the sensor array locations first. However, unlike other networks in which direct time-of-arrival (TOA) measurements might be possible, acoustic distributed network can only obtain time-difference-of-arrival (TDOA) measures indirectly from various sound source anchors. While it is common to employ convex optimization techniques to localize sensor locations in a network with TOA information, it has not been studied properly when it comes to TDOAs. This paper considers the microphone array localization problem in a distributed acoustic network with TDOA measurements. We formulate the inverse problem which applied the known source locations to identify the wireless array configuration and estimate the location for each array. The proposed method formulates a mixed semidefinite programming (SDP) and second-order cone programming (SOCP) relaxation model, and then the acoustic geometry is obtained by solving a linear optimal programming. Furthermore, the characteristics of the optimal solution are studied and exact relaxation conditions are given. Experimental results demonstrate that the proposed mixed model can successfully estimate the sensor locations in noisy and reverberant environments for 2-dimensional and 3-dimensional space, which outperforms other relaxation methods.

In this paper, we study asymptotic behaviors of an estimator of the number of operating sensors in a sensor network based on the Good-Turing estimator. The asymptotic normality, some moderate deviations and deviation inequalities of the estimator are obtained. Our approach is based on the tail probability estimates and moderate deviations for occupancy problems. Applying these asymptotic behaviors, we give a performance analysis for the estimator of the number$N$of operating nodes when the deviations of the estimator are in$(\sqrt {N}, o(N))$. These estimates also provide a method to build confidence interval of$N$.

Speech is a natural user interface for the Internet of Things system. However, the presence of noise affects severely the performance of such system. With the deployment of smart devices with microphones, one can form a powerful acoustic sensor network to enhance the speech via beamforming techniques. On the other hand, reliability of data transmission also determines the beamforming performance, since faulty data will drift the beamformer steering location randomly. Currently, there is no protection scheme for acoustic data transmitted over the wireless network in order to keep steady beamforming performance. In this article, we design a compound distributed beamformer, where nodes are grouped and the system is embedded with blockchain technology to protect the data integrity during transmission. It attempts to provide more possible reliable connections between groups. Simulated experiments show that the distributed beamformer with blockchain protection is able to maintain steady beamforming performance.

Ideas of interval sets come from the lower and upper approximations of rough sets to study a unified structure of rough sets and their generalizations. Starting from the interval sets and their operations, this paper summarizes and analyzes other sets that have similarities with the interval sets or fuzzy interval sets. Our conclusions are that interval sets are mathematically equivalent to shadowed sets and flou sets, respectively, and fuzzy interval sets are mathematically equivalent to interval-valued fuzzy sets and intuitionistic fuzzy sets, respectively.

Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account.

Acoustic source localization has many important applications. Convex relaxation provides a viable approach of obtaining good estimates very efficiently. There are two popular convex relaxation methods using either semi-definite programming (SDP) or second-order cone programming (SOCP). However, the performances of the methods have not been studied properly in the literature and there is no comparison in terms of accuracy and performance. The aims of this article are twofold. First of all, we study and compare several convex relaxation methods. We demonstrate, by numerical examples, that most of the convex relaxation methods cannot localize the source exactly, even in the performance limit when the time difference of arrival (TDOA) information is exact. In addressing this problem, we propose a novel mixed SDP-SOCP relaxation model and study the characteristics of the optimal solutions and its localizable region. Furthermore, an error correction scheme for the proposed SDP-SOCP model is developed so that exact localization can be achieved in the performance limit. Experimental data have been collected in a room with two different array configurations to demonstrate our proposed approach.

In this letter, a hybrid descent method is used to determine a set of filter parameters for a sigmoid filter which attempts to work under various SNR conditions. It overcomes the limitations of the current sigmoid filters that performs effectively only at a single SNR. Results show that significant improvement in terms of better speech qualities can be achieved by the proposed sigmoid filter when working under various SNR conditions.

Beamforming remains to be an important technique for signal enhancement. For applications in open space, the transfer function describing waves propagation has an explicit expression, which can be employed for beamformer design. However, the function becomes very complex in an indoor environment due to the effects of reverberation. In this paper, this problem is discussed. A method based on the image source method (ISM) is applied to model the room impulse responses (RIRs), which will act as the transfer function between source and sensor. The indoor beamformer design problem is formulated as a minimax optimization problem. We propose and study several optimization models based on the L1-norm to design the beamformer. We found that it is advantageous to separate early and late reverberations in the design process and better designs can be achieved. Several numerical experiments are presented using both simulated data and real recordings to evaluate the proposed methods.

Speech recognition has been used in various real-world applications such as automotive control, electronic toys, electronic appliances etc. In many applications involved speech control functions, a commercial speech recognizer is used to identify the speech commands voiced out by the users and the recognized command is used to perform appropriate operations. However, users' commands are often corrupted by surrounding ambient noise. It decreases the effectiveness of speech recognition in order to implement the commands accurately. This paper proposes a multichannel filter to enhance noisy speech commands, in order to improve accuracy of commercial speech recognizers which work under noisy environment. An innovative particle swarm optimization (PSO) is proposed to optimize the parameters of the multichannel filter which intends to improve accuracy of the commercial speech recognizer working under noisy environment. The effectiveness of the multichannel filter was evaluated by interacting with a commercial speech recognizer, which was worked in a warehouse.

For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users' commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a significant improvement regarding speech recognition accuracy.

This paper proposes a genetic algorithm (GA) based beamformer to optimize speech recognition accuracy for a pretrained speech recognizer. The proposed beamformer is designed to tackle the non-differentiable and non-linear natures of speech recognition by employing the GA algorithm to search for the optimal beamformer weights. Specifically, a population of beamformer weights is reproduced by crossover and mutation until the optimal beamformer weights are obtained. Results show that the speech recognition accuracies can be greatly improved even in noisy environments.

Microphone arrays play an important role in noise reduction and speech enhancement. Their algorithms are based on beamforming, which reduces the level of localized and ambient noise signals while minimizing distortion to speech from the desired direction via spatial filtering. This paper describes a class of subband beamforming algorithms. The similarity between different algorithms is discussed. To enhance computational efficiency, the algorithms are implemented in frequency domain. A hardware architecture, with bitwidth optimization, is proposed to support the algorithms. An implementation with 7 instances on a Xilinx XC4VSX55 FPGA at 175MHz can run 41.7 times faster than the corresponding pure software implementation on a 3.2GHz Pentium 4 PC.

Subband adaptive filters have been proposed to avoid the drawbacks of slow convergence and high computational complexity associated with time domain adaptive filters. However, subband processing causes signal degradations due to aliasing effects and amplitude distortions. This problem is unavoidable due to further filtering operations in subbands. In this letter, the problems of aliasing effect and amplitude distortion are studied. Prototype filters which are optimized with respect to those properties are designed and their performances are compared. Moreover, the effect of the number of subbands, the oversampling factors and the length of the prototype filter are also studied. Using the multicriteria formulation, all Pareto optimums are sought via the nonlinear programming technique. We find that the prototype filter designed via the Kaiser window provides the best overall performance among the methods we studied. Also, there is a critical oversampling factor beyond which the improvement of performance is diminishing. Finally, if the length of the prototype filter increases with the number of subbands, an increase in the number of subbands will not deteriorate the performance.

Broadband microphone arrays has important applications such as hands-free mobile telephony, voice interface to personal computers and video conference equipment. This problem can be tackled in different ways. In this paper, a general broadband beamformer design problem is considered. The problem is posed as a Chebyshev minimax problem. Using the l/sub 1/-norm measure or the real rotation theorem, we show that it can be converted into a semi-infinite linear programming problem. A numerical scheme using a set of adaptive grids is applied. The scheme is proven to be convergent when a certain grid refinement is used. The method can be applied to the design of multidimensional digital finite-impulse response (FIR) filters with arbitrarily specified amplitude and phase.

A new design method for broadband microphone arrays is presented. Using sequences of calibration signals, the method is able to design finite-impulse response (FIR) filters with specific performance. The method can control and adjust the speech distortion, noise suppression, and echo cancellation directly. It turns out that a significantly shorter filter length can be applied to achieve better overall performance than the least-squares method or the signal-to-noise plus interference method.

In using the B-spline network for nonlinear system modeling, owing to a lack of suitable theoretical results, it is quite difficult to choose an appropriate set of knot points to achieve a good network structure for minimizing, say, a minimum error criterion. In this paper, a novel knot-optimizing B-spline network is proposed to approximate the general nonlinear system behavior. The knot points are considered to be independent variables in the B-spline network and are optimized together with the B-spline expansion coefficients. The simulated annealing algorithm with an appropriate search strategy is used as an optimization algorithm for the training process in order to avoid any possible local minima. Examples involving dynamic systems up to six dimensions in the input space to the network are solved by the proposed method to illustrate the effectiveness of this approach.