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In the realm of smart communication systems, where the ubiquity of 5G/6G networks and IoT applications demands robust data confidentiality, the cryptographic integrity of block and stream cipher mechanisms plays a pivotal role. This paper focuses on the enhancement of cryptographic strength in these systems through an innovative approach to generating substitution boxes (S-boxes), which are integral in achieving confusion and diffusion properties in substitution–permutation networks. These properties are critical in thwarting statistical, differential, linear, and other forms of cryptanalysis, and are equally vital in pseudorandom number generation and cryptographic hashing algorithms. The paper addresses the challenge of rapidly producing random S-boxes with desired cryptographic attributes, a task notably arduous given the complexity of existing generation algorithms. We delve into the hill climbing algorithm, exploring various cost functions and their impact on computational complexity for generating S-boxes with a target nonlinearity of 104. Our contribution lies in proposing a new cost function that markedly reduces the generation complexity, bringing down the iteration count to under 50,000 for achieving the desired S-box. This advancement is particularly significant in the context of smart communication environments, where the balance between security and performance is paramount. [ABSTRACT FROM AUTHOR]

This paper presents the design of a broadband and high-power amplifier with bounded performance technique (BPT). Like the real frequency technique (RFT) or the simplified RFT (SRFT), BPT achieves broadband performance via matching at some in-band discrete real frequencies. It solves some design issues that are not addressed in the RFT and the SRFT. Among them are the inclusion of the design specifications in the device selection and design process and the possible degradation of operation metrics that are not optimized. BPT also avoids complicated analytic mathematics by using circuit prototypes. In this paper, we show the design of an amplifier with BPT. It has a 3-dB compression power of at least 100 W and a drain efficiency of at least 50% over the band 1–2.8 GHz or 94.7% fractional bandwidth. We also show the design of the same amplifier with the traditional SRFT and compare its expected performance with that designed with BPT. [ABSTRACT FROM AUTHOR]

Optimizing the operational parameters and control of the power system in steady-state conditions is a crucial issue in reducing the costs of power generation and operation. In the case of long-term operation of a power system, besides aiming to minimize power generation costs, the cost of damage caused by load shedding also needs to be considered. This paper presents the optimization of the total cost of a power system including minimizing the generation cost function of power plants or power companies and minimizing the damage cost function caused to customers due to load shedding or power outages. At the same time, the objective function must also ensure the constraints on the operating conditions of the power system. This contributes to maintaining the continuity of the power supply to critical loads and minimizing damage. Base loads, priority loads, or loads that are not allowed to be shed are considered as constraints. The optimization problem is addressed by using the Particle Swarm Optimization (PSO) algorithm and the Cuckoo Search Algorithm (CSA). The IEEE 30-bus test system is applied to validate the reduction in total cost. The result comparison shows that when applying the CSA, the total cost is significantly reduced by 3.75% in comparison with the PSO algorithm. The algorithms are implemented in Matlab to demonstrate the efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Microgrid's voltage regulation is of particular importance during both grid-connected and islanded modes of operation. Especially, during the islanded mode, when the support from the upstream grid is lost, stable voltage regulation is vital for the reliable operation of critical loads. This paper proposes a robust and data-driven control approach for secondary voltage control of AC microgrids in the presence of uncertainties. To this end, unfalsified adaptive control (UAC) is utilized to select the best stabilizing controller from a set of pre-designed controllers with the minimum knowledge required from the microgrid. Two microgrid test systems are simulated in MATLAB to verify the effectiveness of the proposed method under different scenarios like load change and communication link failure. [ABSTRACT FROM AUTHOR]

The paper deals with the new design procedure for PID-D robust controller with state/output derivative feedback to stabilize the closed-loop system using linear matrix inequality (LMI) and LMI regions with common in the frame of $H_2$ performance quadratic cost functions known as $(Q,S,R)$. The designed robust controller ensures robust properties of the closed loop system for linear time invariant and linear time variant gain scheduled plant descriptor systems. The robust controller design procedure is based on the extended original derivative of Lyapunov function and to obtain the convex conditions with respect to uncertainties, gain scheduled variables, and ensures that proposed new method guarantees less conservativeness to the design procedure we have introduced using the auxiliary matrices. The obtained results should be utilized to design power system stabilizer for synchronous generators and so on. The obtained robust PI-D controller with output and state/output derivative feedback for uncertain polytopic systems ensures that all closed-loop eigenvalues are lying in the defined LMI region. Two examples illustrate the effectiveness of the new design procedure. [ABSTRACT FROM AUTHOR]

Economic dispatch with valve-point effect (EDVPE) considered is presented as a more accurate model of the real problem compared to the conventional economic dispatch model. It is basically a non-convex, non-differentiable, and multi-modal optimization model with many local minima. Part I of the paper focuses on the local minimum analysis of the EDVPE. The analysis indicates that a local minimum consists of the singular points, the small convex regions, and the output of a slack unit that is dispatched to balance the load demand. Two types of local minima are identified and the second type could be ignored. To verify the rationality of the analyses, a traverse search has been performed to solve the EDVPE with and without considering the transmission loss on different test systems. All the simulation results support the analysis given in the paper. To effectively solve the EDVPE on a large-scale power system, based on the analysis presented in this paper, a new method, dimensional steepest decline method, is proposed in Part II of the paper. [ABSTRACT FROM PUBLISHER]

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds. [ABSTRACT FROM AUTHOR]

This paper is about the effectiveness of the Cobb-Douglas (C-D) production function in industrial optimization, estimating the number of factors used in the production process of the water industry, for instance, capital and human labor. Moreover, we have modeled a nonlinear optimization problem for a local water industry using two and three factors of production. For this purpose, we have taken into account the Cobb-Douglas production function with different production factors using the Lagrange multiplier method with the ordinary least squares method. In the course of the solution, a linear function is used to calculate the cost function, and the C-D production function is used to calculate the production function. The Lagrange multiplier method with the ordinary least squares method is then used to solve the constrained optimization problem for the product of production. Furthermore, we compared the outcomes from both examples of two- and three-factor C-D production functions in order to validate the Lagrange multiplier method for the C-D production function. Moreover, the three-factor C-D production function is solved by the Lagrange multiplier method with the ordinary least squares method, which provides optimal results as compared to previous studies in literature. The validity of the proposed methodology is explained by using the products of a local production industry in Pakistan. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to designing a CMOS inverter using the Mayfly Optimization Algorithm (MA). The MA is utilized in this paper to obtain symmetrical switching of the inverter, which is crucial in many digital electronic circuits. The MA method is found to have a fast convergence rate compared to other optimization methods, such as the Symbiotic Organisms Search (SOS), Particle Swarm Optimization (PSO), and Differential Evolution (DE). A total of eight different sets of design parameters and criteria were analyzed in Case I, and the results confirmed compatibility between the MA and Spice techniques. The maximum discrepancy in fall time across all design sets was found to be 2.075711 ns. In Case II, the objective was to create a symmetrical inverter with identical fall and rise times. The difference in fall and rise times was minimized based on Spice simulations, with the maximum difference measuring 0.9784731 ns. In Case III, the CMOS inverter was designed to achieve symmetrical fall and rise times as well as propagation delays. The Spice simulation results demonstrated that symmetry had been successfully achieved, with the minimum difference measuring 0.312893 ns and the maximum difference measuring 1.076540 ns. These Spice simulation results are consistent with the MA results. The results conclude that the MA is a reliable and simple optimization technique and can be used in similar electronic topologies. [ABSTRACT FROM AUTHOR]

In this correspondence paper, we address the direct position determination (DPD) problem for wideband stationary sources using time-delayed and Doppler-stretched signals collected by multiple moving receivers. The source signals are assumed to be zero-mean, stationary, and Gaussian distributed, which is reasonable in most passive sonar and acoustics scenarios. A scaled formulation is used for wideband signals to elaborate the Doppler effect, as significant biases will be introduced if such signals are approximated with narrowband models directly. In this paper, wideband signals are decomposed into a small number of low-dimensional components with high accuracy, and a computationally efficient DPD method is proposed by jointly utilizing the time delay and scaling information of received signals. In order to evaluate the performance of the proposed DPD method, the Cramér Rao lower bound for source localization in the considered scenario is derived. Numerical results validate the approximation of wideband signals and demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

We present an approach to extend the functionality and the use of kernel machines in image processing applications. We introduce a novel way to design spatial kernel machines with spatial properties and demonstrate how those newly introduced spatial properties enhance the possibilities of the use of kernel machines in image processing applications as a proof of concept. In this paper, we demonstrate four particular extensions: 1) how to model shapes efficiently with spatially computed kernel parameters in a geometrically scalable way; 2) how to visualize the kernel parameters precisely and intuitively on binary 2D shapes; 3) how to construct a one-class classifier from the binary classifier in a straightforward manner without re-training; and 4) how to use the computed kernel parameters for filtering. The existing literature on kernel machines mostly focuses on estimating the optimal kernel parameters via additional cost function(s). In this paper, instead of employing an additional cost function to estimate the kernel-related parameters, we investigate on an analytical solution to predict the actual kernel parameters locally and show how to build a spatial kernel machine with our analytical approach. Classical kernel machines do not perform well on precise shape modeling with a low number of support vectors as demonstrated in this paper. However, we demonstrate and visualize that our analytical approach provides a natural means to relate the kernel parameters to the 2D shapes for sparse shape modeling, where the shape boundary represents the decision boundary. For that, we incorporate the selected kernel function’s geometric properties as an additional constraint into the classifier’s optimization problem by defining an easy-to-explain and intuitive concept: similarity domains. In our experiments, we study and demonstrate how the resulting new kernel machine enhances the capabilities of the classical kernel machines with applications on shape modeling, (geometrically) scaling the non-linear decision boundary at various scales and precise visualization of the kernel parameters in 2D images. [ABSTRACT FROM AUTHOR]

Electrical capacitance tomography exhibits great potentials in the visualization measurement of industrial processes, and high-precision images are of great significance for the reliability and usefulness of measurement results. In this paper, we propose a deep learning-based inversion method to ameliorate the reconstruction accuracy. With the aid of the deep learning methodology, the prior from the images reconstructed by a certain imaging technique to the true images is abstracted and stored in the deep extreme learning machine. A new cost function is constructed to encapsulate the prior from the proposed deep learning model and the domain expertise about imaging targets, and the split Bregman algorithm and the fast iterative shrinkage thresholding technique are combined into a new numerical method to effectively solve it to get the final reconstruction. The numerical and experimental results validate that the inversion method proposed in this paper reduces the reconstruction artifacts and deformations and leads to the much improvement in the imaging quality. [ABSTRACT FROM AUTHOR]

For the short-term high torque output, an improved model predictive direct torque control (MPDTC) method with field enhancing is proposed to increase torque. In order to avoid the possible loss of synchronism while increasing torque, the MPDTC method limits the load angle in the cost function. Under the constant load angle limitation, the improved method increases the torque output capability by increasing the magnetic field. This paper starts from the torque calculation, theoretically analyzes the torque-load angle characteristics, and illustrates the flux vector trajectory with the voltage limit circle and current limit circle. The proposed method maintains the dynamic and static performance of the traditional MPDTC method while ensuring the system stability. The experimental results show that the torque capacity of the proposed method has been improved, and it has stable extreme output torque. So, some applications, such as electric load simulators, can choose the motor with small capacity and small inertia, thereby weakening negative effects of the inertia torque. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) considering valve-point effect, multiple fuel options, prohibited operating zones of generation units is a more accurate model compared to a conventional ED model. It is non-smooth and thus evolutionary algorithms (EAs) are so far the only feasible approaches for the model. In Part II of the paper, a new method, the dimensional steepest decline method (DSD), is proposed for the ED with non-smooth objectives. The DSD is based on the local minimum analysis of the ED problem presented in Part I of the paper. The fuel cost's decline rate between each two adjacent singular points is utilized to find the optimal solutions in a serial sequence. The computational complexity of the DSD is analyzed. The DSD has been applied to solve different types of ED problems on different test systems, including large-scale systems. The simulation results show that DSD can obtain more accurate solutions and consume much less time and its advantage is more obvious on large-scale systems, in comparison with the state-of-art EAs. [ABSTRACT FROM PUBLISHER]

In Machine Learning, prediction quality is usually measured using different techniques and evaluation methods. In the regression models, the goal is to minimize the distance between the actual and predicted value. This error evaluation technique lacks a detailed evaluation of the type of errors that occur on specific data. This paper will introduce a simple regularization term to manage the number of over-predicted/under-predicted instances in a regression model. [ABSTRACT FROM AUTHOR]

We investigate the matrix function optimization under the orthonormal constraint on the matrix variable. By introducing an index-notation-arrangement-based chain rule (I-Chain rule), we obtain the gradient of the cost function and propose a revisited orthonormal-constraint-based projected gradient method to locate a minimum of an objective/cost function of matrix variables iteratively subject to orthonormal constraint. To guarantee the convergence the proposed method, existing schemes require the gradient can be represented by the multiplication of a symmetrical matrix and the matrix variable itself. This condition has been relaxed in this paper. New techniques are proposed to establish the convergence property of the iterative algorithm. Simulation results show the effectiveness of our framework. This paper allows more extensive applications of matrix function optimization problems in science and engineering. [ABSTRACT FROM AUTHOR]

Decomposition is a technology-independent process, in which a large complex function is broken into smaller, less complex functions. The costs of two-level or factored-form representations (cubes and literals) are used in most decomposition methods, as they have a high correlation with the area of cell-based designs. However, this correlation is weaker for field-programmable gate arrays (FPGAs) based on look-up tables. Furthermore, local optimizations have limited power due to the structural bias of the circuit descriptions. This paper tries to reduce the structural biasing by remapping the look-up table network and decomposing the derived functions using the support as cost function. The proposed method improves the FPGA mapping results of a commercial tool for the 20 largest MCNC benchmarks, with gains of 28% in delay plus 18% in area when targeting delay, and a reduction of 28% in area plus 14% in delay with area as cost function. Results with 23% less area and 6% less delay are obtained after physical synthesis (post place-and-route). Moreover, 12 of the best known results for delay (and 3 for area) of the EPFL benchmarks are improved. [ABSTRACT FROM AUTHOR]

This paper presents a self-learning strategy to control the deactivation of cylinders in large displacement automotive engines. Cylinder deactivation is a useful strategy to reduce emissions and improve fuel consumption without limiting the maximum engine power. A cylinder deactivation algorithm has to balance the target of reducing emissions with drivability concerns. Changing the cylinder configuration at the wrong time or too often negatively affects drivability and fuel efficiency. The paper proposes a self-learning algorithm inspired by the Markov Chain formalism. The proposed algorithm predicts the future fuel consumption and determines the best cylinder configuration based on that prediction. The prediction algorithm is learnt online using past inputs and thus adapts to the specific driver and conditions. Two versions of the algorithm are proposed that use two different inputs. The approaches are validated in simulation on an engine test-bed, showing that the self-learning algorithm yields fuel savings up to 10% without affecting drivability in the New European Driving Cycle. [ABSTRACT FROM AUTHOR]

Thermostatically controlled loads (TCLs) are regarded as one of the promising resources for suppressing power fluctuations due to renewable energy (RENs). However, due to the great burdens of fully considering each users’ characteristics, it is difficult to achieve unity of the individual optimal decisions and global optimum in demand-side management (DSM). This paper proposes a game-theoretic DSM that can optimize the global power consumption schedule by individual TCL user's optimization. By integrating the prediction of REN outputs into the pricing mechanism, the proposed DSM can guide the users to make their best power consumption schedules along with intermittent REN generations, thus to smooth the tie-line power of microgrids. In this paper, a novel pricing mechanism is firstly developed based on the concave N-person game theory, which is more adaptive and flexible compared with existing game-theoretic DSM. Then, an individual's power consumption optimization and its simplified model are developed considering the constraints of the TCL model and personal preferences. The simplified model can be easily solved and achieve a fast solution in the power consumption game. An implementation framework of the proposed DSM is further developed for practical application. Finally, numerical studies verify the effectiveness of the proposed models and methods. [ABSTRACT FROM AUTHOR]

Efficient exploration in multi-robot systems is significantly influenced by the initial start positions of the robots. This paper introduces the hybrid cheetah exploration technique with intelligent initial configuration (HCETIIC), a novel strategy explicitly designed to optimize exploration efficiency across varying initial start configurations: uniform distribution, centralized position, random positions, perimeter positions, clustered positions, and strategic positions. To establish the effectiveness of HCETIIC, we engage in a comparative analysis with four other prevalent hybrid methods in the domain. These methods amalgamate the principles of coordinated multi-robot exploration (CME) with different metaheuristic algorithms and have demonstrated compelling results in their respective studies. The performance comparison is based on essential measures such as runtime, the percentage of the explored area, and failure rate. The empirical results reveal that the proposed HCETIIC method consistently outperforms the compared strategies across different start positions, thereby emphasizing its considerable potential for enhancing efficiency in multi-robot exploration tasks across a wide range of real-world scenarios. This research underscores the critical, yet often overlooked, role of the initial robot configuration in multi-robot exploration, establishing a new direction for further improvements in this field. [ABSTRACT FROM AUTHOR]

One of the ways to increase the reliability of the system is redundancy allocation to the system. The standby redundancy is divided into three categories, cold, hot, and warm, depending on the environmental conditions in which they are placed in standby mode. The cold standby redundancy is inactive in standby mode, and the possibility of its failure in this mode is considered zero. A hot standby redundancy is active in standby mode and has the same failure rate as active system components. A warm standby redundancy is also active in standby mode, but it is not subjected to the same stress as the active system components as a hot standby redundancy. In this article, we investigate the reliability of stress-strength in k out of n:G system in an H-Weibull model equipped with three types of standby redundancy and we get results about the time to add the standby component to this system. The baseline distribution function of the model is H, which is the distribution of the stress system. The contents of this article are categorized as follows. In the second part, in the general case, we will calculate the reliability of stress-strength in a k out of n:G system in an H-Weibull model equipped with a standby redundancy in H-Weibull models. In addition, we obtain a closed form for the stress-strength reliability of these systems under the exponential baseline distribution in the three standby states of cold, hot, and hot. In the third part, we study the time and cost of adding the standby component to this system. In this section, by introducing the cost function, we obtain the optimal time to add the standby component of the system with the aim of simultaneously reducing the costs and increasing the reliability of the stress-strength model under different values of the model parameters. In the last part, the conclusion of the content of the article is presented. Material and Methods In this paper, we present the general case for adding a standby component to a k out of n:G stress-strength system. Then, in the H-Weibull model, we investigate the reliability behavior of stress-strength in this system. A k out of n:G system is active, whenever at least k components out of n components are active. Consider a k out of n:G system with independent identically distributed (i.i.d) Y₁ ،...، Yn with distribution function G. Assume that the components of the above system are independent of each other putting into a common random stress Z with cumulative distribution function H and random variables Z and Yi for i = 1 ،...، n. In this case, the stress-strength reliability of this system is given as R(n. k) = P(ZYn-k+1n), where Yi:n, is i th order statistic in the random sample Y₁ ،...، Yn. Suppose that the random variable X is the lifetime of a standby redundant with the distribution function F in normal environmental conditions and with the cumulative distribution function F(γ(t)) in the standby state, where γ(t) is any ascending function meeting two requirements for every t > 0, γ(0) = 0 and γ(t) ≤t. On the other hand, the ascending function w(t) is the transformed age of the standby redundant, from the standby state to the normal condition applying to every t > 0 in two conditions w(0) = 0 and w(t) ≤t. The hot and cold ready add-ons are special modes of the above model and are obtained when w(t) = γ(t) = 0 and w(t) = γ(t) = t, respectively. Letk out of n:G mentioned stress-strength system equipped with the standby redundant with lifetime X. Here, the standby redundancy enters the system operation after observing the (n-k-m+1)th failure among the system components, where m = 0...n-k, and the system until the failure of the (m + 1)th component out of m+k remaining components is kept active. Results and Conclusion In this article, the importance of the time allocation of the standby redundant to the k-out-of-n:G stress-strength system and its effect on the stress-strength reliability and costs in the H-Weibull model are investigated. Also, the behavior of the cost function based on the H-Weibull model parameters is studied. The optimal time to add the standby component to the system performance in a k-out-of-n:G stress-strength system in the H-Weibull model is presented. Based on the results of this article, the adding time of the standby component with the aim of increasing the stress-strength reliability is dependent on the parameters of H-Weibull model, and the time changes by changing these parameters. On the other hand, the time to add the standby component to the stress-strength system in the model with the aim of increasing the stress- strength reliability and reducing the system costs simultaneously, in addition to the system parameters is dependent on the number of system components and the value of k. In any case, we can conclude that for large values of n and k, the optimal time to add is m = 0 and for small values of k and large n, this value is usually m=n k and based on the distance between the values of n and k from each other, the value of m will be changeable. [ABSTRACT FROM AUTHOR]

Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective. [ABSTRACT FROM AUTHOR]

This paper presents an approach to obtain high torque density in induction motor (IM) design by optimizing the search algorithms and processes. Optimal design of electrical machines has many sub-optimal peaks and requires significant computation time. To solve this problem, this paper proposes a new cost pattern value method (CPVM) for local search algorithms employed in optimal design. The CPVM concept is an intelligent strategy that digitizes the gradient over the searching region by sharing information between neighboring points. When the algorithm converges quickly to local optimum by a normalized weight method, it provides a mechanism to escape from that region. The validity of the proposed approach was evaluated using a benchmark function, and it was applied to optimal design of a 260 kW IM to maximize torque production based on a finite-element analysis. For better accuracy, we employ slip-frequency analysis compatible with vector control for an IM numerical analysis. [ABSTRACT FROM AUTHOR]

This paper presents a multi-stage series model predictive control (MS-MPC) for PMSM drives in order to further improve the control performance of MPC. Firstly, the basic principle of conventional MPC is introduced. Then, the multi-stage series MPC is proposed in this paper, in which the cost functions of different prediction instant are adopted to form series control structure. In this method, the current prediction trajectory of one voltage vector is extrapolated to multiple control periods, and the control performances of every voltage vector during multiple control periods are evaluated by multi-stage series cost functions. Additionally, the implementation process of the proposed method is described. Finally, the comparison experiments between the conventional MPC and the proposed method show that the proposed method can effectively reduce the switching frequency under the same control frequency. In addition, the improvement of current control performance can be achieved by the proposed method under the condition of the same switching frequency. [ABSTRACT FROM AUTHOR]

The arc permanent magnet motor (arc motor) is widely used in large telescope for its high efficiency and high power density. Due to the unique structure, the periodic end torque, cogging torque and flux harmonics will cause certain speed ripples which can affect the performance of the drive system. To solve these problems, the paper proposes a novel model predictive control (MPC) based on extended state observer and iterative learning control, where the MPC is equipped in speed loop and the disturbances estimated by the conventional extended state observer (ESO) are fed forward to the controller to improve the ability of disturbance rejection. A position dependent iterative learning control (ILC) is used to estimate the periodic disturbances at different speeds. The MPC calculates the q axis current reference with the compensation of ESO and ILC, which can have a fast response and disturbance rejection ability for periodic and nonperiodic disturbances. The stability of the proposed method is demonstrated by theoretical analysis. The experimental results validate the effectiveness of the proposed method at different speeds. [ABSTRACT FROM AUTHOR]

Finite-control-set model predictive current control (FCS-MPCC) always has large steady-state fluctuation and computational burden. In this paper, a novel FCS-MPCC without a modulator to drive permanent magnet synchronous hub motors (PMSHMs), which combines virtual vectors expansion scheme and duty cycle control was proposed. The lack of a modulator reduces the complexity of the control system. The virtual vectors are synthesized by using active vectors, which improve the accuracy of voltage selection, and further improve PMSHM's steady-state performance and reduce current harmonics. The duty cycle control uses a zero vector to obtain better steady-state performance. However, the duty cycle of the virtual vectors is limited by the synthesis method, and further analysis is needed. A new calculation process is proposed to reduce the amount of calculation. The deadbeat principle is used to get reference voltage which determines sectors. Then, the best voltage vector in the selected sector is determined by the predetermined cost function. The traditional MPCC and the duty cycle MPCC (DCMPCC) are used as a comparison item to compare with the proposed method to illustrate its effectiveness. Results confirm that improved MPCC has good steady-state performance while maintaining a fast dynamic response. [ABSTRACT FROM AUTHOR]

A lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of deep convolutional neural networks (DCNNs). In the recent works, the texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruction is still not capable of modeling facial texture with high-frequency details. In this paper, we take a radically different approach and harness the power of generative adversarial networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images. That is, we utilize GANs to train a very powerful facial texture prior from a large-scale 3D texture dataset. Then, we revisit the original 3D Morphable Models (3DMMs) fitting making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. In order to be robust towards initialisation and expedite the fitting process, we propose a novel self-supervised regression based approach. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, to the best of our knowledge, facial texture reconstruction with high-frequency details. [ABSTRACT FROM AUTHOR]

Detecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions. This paper proposes a novel, yet effective outlier learning algorithm that is based on decomposing the full attributes space into different combinations of subspaces, in which the 3D-vectors, representing the data points per 3D-subspace, are rotated about the geometric median, using Rodrigues rotation formula, to construct the overall outlying score. The proposed approach is parameter-free, requires no distribution assumptions and easy to implement. Extensive experimental study and comparison are conducted on both synthetic and real-world datasets with six popular outlier detection algorithms, each from different category. The comparison is evaluated based on the precision @s, average precision, rank power, AUC ROC and time complexity metrics. The results show that the performance of the proposed method is competitive and promising. [ABSTRACT FROM AUTHOR]

Copyright of China Petroleum Machinery is the property of China Petroleum Machinery Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The process capability index (PCI) has been developed as a useful instrument for measuring how well a product matches customer expectations while also assessing the performance of a process. We are aware that when the quality characteristics of the processes follow normal distribution, conventional PCIs produces superior results. However, these traditional indices could not yield reliable findings when assessing nonnormally distributed processes, which could make decision-making more difficult. In this paper, we take into account the C N p m k c process capacity index, which may be used for both normally distributed and nonnormally distributed processes. We have employed 10 traditional methods of estimation to estimate the PCI C N p m k c when the process has a logistic-exponential distribution, and the performances of these traditional estimates of the index C N p m k c are compared in terms of their mean squared errors through a simulation exercise. Then, we create five PCI C N p m k c bootstrap confidence intervals and contrast them based on their average widths and related coverage probabilities. In order to demonstrate the applicability of the suggested methods of estimation, two data sets pertaining to the electronic industries are re-analyzed. [ABSTRACT FROM AUTHOR]

Convex hull pricing has been introduced recently to increase transparency and reduce uplift payments for the U.S. wholesale markets. A convex primal formulation approach is one of the most efficient methods to obtain a high-quality convex hull price. Even though significant progress has been made, the co-optimization of energy and ancillary services is much more challenging due to the discrete nature in formulating ancillary services, for example, the regulating reserve commitment variables, which are binary introduced by some Independent System Operators (ISOs), such as Midcontinent ISO (MISO), to capture the special characteristics of certain generators, such as combined-cycle units. In this paper, we propose convex primal formulations for convex hull pricing considering regulating, spinning, and online/offline-supplemental reserves, in which the regulating reserve commitment variables are considered. Accordingly, we can solve a linear program, instead of a mixed-integer program, which is much harder to solve, to derive the minimum uplift payments and exact convex hull price for the case without general ramping constraints. For the case with general ramping constraints, an upper bound of the minimum uplift payment can also be derived. The final computational experiments on MISO cases verify the effectiveness of our approach in solution quality and computational time. [ABSTRACT FROM AUTHOR]

High-speed train industry is in the stage of rapid development, and the reliability allocation strategy is helpful to improve the reliability of the system and reduce the economic cost of components. In this paper, the reliability allocation of gearbox transmission system of highspeed train is studied. Combining AGREE allocation method and reliability allocation method based on cost function, the reliability allocation of high-speed train gear transmission system is carried out. Considering the quantity, importance, cost and complexity of various parts of high-speed train gear transmission system, the feasibility factor of improving reliability based on the cost function allocation method was improved to acquire the improved cost function, and the reliability optimization allocation model based on the improved cost function was established. Compared with AGREE allocation method and the traditional allocation method based on cost function, the improved optimization allocation strategy proposed in this paper is more suitable for high-speed train gear transmission system. [ABSTRACT FROM AUTHOR]

In this paper, a finite-control-set model predictive direct torque control (FCS-MPDTC) method is proposed to reduce the torque ripple of a segmented switched reluctance motor (SSRM) at the low-speed stage. Firstly, the prediction dynamic model is established, and the phase torque can be predicted by detecting phase current and rotor position signals. Then, the principle of power converter and selection of voltage vectors are introduced and presented. Considering torque ripple reduction, flux tracking performance and copper losses reduction, the cost function with phase torque, the amplitude of flux linkage and phase current, is established to select optimal voltage vector to control the power converter. In addition, the torque sharing function (TSF) is employed to distribute total torque to phase torque for further torque ripple reduction. Finally, the direct torque control (DTC) is selected as the comparison method, performance of MPDTC is verified by simulation and experiment results. It can be found that the proposed MPDTC can achieve lower torque ripple and copper losses, and high robustness. [ABSTRACT FROM AUTHOR]

In this paper, we devise a communication-efficient decentralized algorithm, named as communication-censored alternating direction method of multipliers (ADMM) (COCA), to solve a convex consensus optimization problem defined over a network. Similar to popular decentralized consensus optimization algorithms such as ADMM, at every iteration of COCA, a node exchanges its local variable with neighbors, and then updates its local variable according to the received neighboring variables and its local cost function. A different feature of COCA is that a node is not allowed to transmit its local variable to neighbors, if this variable is not sufficiently different to the previously transmitted one. The sufficiency of the difference is evaluated by a properly designed censoring function. Though this censoring strategy may slow down the optimization process, it effectively reduces the communication cost. We prove that when the censoring function is properly chosen, COCA converges to an optimal solution of the convex consensus optimization problem. Furthermore, if the local cost functions are strongly convex, COCA has a fast linear convergence rate. Numerical experiments demonstrate that, given a target solution accuracy, COCA is able to significantly reduce the overall communication cost compared to existing algorithms including ADMM, and hence fits for applications where network communication is a bottleneck. [ABSTRACT FROM AUTHOR]

This paper considers real-time energy-optimal trajectory generation for a servomotor system that performs a single-axis point-to-point positioning task for a fixed time interval. The servomotor system is subject to acceleration and speed constraints. The trajectory generation is formulated as a linear-constrained optimal control problem (LCOCP), and the Pontryagin's maximum principle is applied to derive necessary optimality conditions. Instead of solving multipoint boundary value problems directly, this paper proposes a novel real-time algorithm based on two realizations: solving the LCOCP is equivalent to determining an optimal time interval of the speed-constrained arc and solving a specific acceleration-constrained optimal control problem (ACOCP), and solving an ACOCP is equivalent to determining optimal switch times of acceleration-constrained arcs and solving a specific two-point boundary value problem (TBVP). The proposed algorithm constructs sequences of time intervals, ACOCPs, switch times, and TBVPs, such that all sequences converge to their counterparts of an optimal solution of the LCOCP. Numerical simulation verifies that the proposed algorithm is capable of generating energy-optimal trajectories in real time. Experiments validate that the use of energy-optimal trajectories as references in a servomotor system does not compromise tracking performance but leads to considerable less energy consumption. [ABSTRACT FROM PUBLISHER]

This paper presents nonlinear optimization-based position and speed estimation scheme for IPMSM drives with arbitrary signal injection generated by inherent switching ripples associated with finite control set model predictive control (FCSMPC). The existing standard sensorless techniques are not suitable for FCSMPC which applies voltage vectors directly to an electrical machine without a modulator. The proposed method optimizes the nonlinear cost function derived from the standard IPMSM model with respect to position and speed at every sampling interval. This method can be applied to any type of signal injection and, hence, an ideal candidate for sensorless FCSMPC. In this method, the signal injection is needed only to generate persistent excitation to maintain the observability at low speeds. A strong persistent excitation is always present with FCSMPC except at standstill where the control applies null vector when the reference currents are zero. This situation is overcome in this paper by introducing a small negative $d$ -axis current at standstill. Thus, the proposed method can estimate the position and speed over a wide speed range starting from standstill to the rated speed without a changeover or additional signal injection. This paper also presents detailed convergence analysis and proposes a compensator for standstill operation that prevents converging to saddle and symmetrical solutions, and therefore, also eliminates the well-known ambiguity of $\pi$ rad in position estimation. The performance of the proposed sensorless scheme is experimentally verified for a wide range of operating conditions. [ABSTRACT FROM AUTHOR]

Microgrids incorporate an increasing number of distributed energy resources (DERs), which induce a higher variability and faster dispatch capabilities in power systems. This paper proposes a two-layer real-time scheduling model for microgrids, based on approximate future cost function (AFCF), where the future cost represents the opportunity cost for the microgrid operation in subsequent periods. At the upper layer, the look-ahead rolling scheduling is adopted to optimize microgrid operations, in which the future cost function (FCF) in deterministic and stochastic scenarios is approximated by a piecewise linear function. At the lower layer, a real-time parameter updating strategy based on real-time data is proposed. In this case, the real-time scheduling readjusts the look-ahead schedule using the immediate cost in the current period and the future cost calculated by the updated AFCF. The proposed two-layer real-time scheduling model uses an offline optimization, in which most of the computation tasks are completed at the upper layer, and applies a real-time optimization, in which the time-consuming problem is avoided at the lower layer. The effectiveness of the proposed two-layer real-time scheduling model of microgrids is validated by using a grid-connected microgrid system. For comparison, other existing real-time scheduling methods are also implemented in the same microgrid system. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed continuous-time optimization problem, which consists of a group of agents with variant local cost functions. An adaptive consensus-based algorithm with event triggering communications is introduced, which can drive the participating agents to minimize the global cost function and exclude the Zeno behavior. Compared to the existing results, the proposed event-based algorithm is independent of the parameters of the cost functions, using only the relative information of neighboring agents, and hence is fully distributed. Furthermore, the constraints of the convexity of the cost functions are relaxed. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic path planning method for the UAV that can avoid both static and moving obstacles. The condition with sudden threats can better reflect the real situation of the UAV in the real environment. First of all, the A* algorithm is adopted to generate an optimal path in a known environment in this method. Then, in the situation of static sudden threats, a series of candidate paths are generated by the principle of cubic spline second-order continuity. In order to make the static sudden threat at the center of a cluster of candidate paths, they need to be adjusted. After that, this path cluster completely surrounds the sudden threat and has symmetry about the sudden threat. When encountering a sudden threat of movement, factors such as the speed, acceleration and certain parameters of the movement obstacle or the UAV are considered, and a correlation model of the dynamic sudden threat is established. Finally, the total cost function is established to select the optimal obstacle avoidance path, and the total cost function contains four sub-cost functions, they are static security cost function, smoothness cost function, consistency cost function and dynamic security cost function. The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes and investigates a novel near-optimal finite-control-set model predictive control (NOP-MPC) algorithm to control the grid-connected, pulsewidth-modulator-driven voltage source converters with LCL filters. Exploiting the convex and elliptical paraboloid properties of the cost error, NOP-MPC adopts a systematic iterative algorithm within each control cycle to progressively synthesize finite sets of virtual voltage vectors (VVs) for the control optimization stage. The synthesis has the inherent features of respecting the converter voltage limits and converging the sets of VV candidates toward the global optimal point. The fixed-switching-frequency feature of NOP-MPC is expected to ease the LCL filter design. Effects of computational delay, pulsewidth modulation delay, and deadtime are considered and compensated successfully. A two-vector-variable cost function is used to actively damp the inherent LC resonance through an adjustable, weighting-factor-based damping level. This paper is substantiated by theoretical consideration, simulation and experimental results, parameter sensitivity study, and a comparative study with the standard finite-control-set model predictive control that uses only actual VVs. [ABSTRACT FROM AUTHOR]

In this paper, the noncooperative optimal control problem is investigated for a class of discrete time-varying networked control systems subject to exogenous nonlinear disturbances. To relive the transmission burden in the sensor-to-controller channel, the Round-Robin protocol is adopted to schedule the sensor transmissions. On the other hand, the controller operates in an event-triggered manner so as to reduce the transmission frequency and thereby preserving the energy in the controller-to-actuator channel. In the presence of the underlying scheduling and triggering mechanism, it is literally impossible to acquire the accurate value of the individual cost function for each controller and, as an effective alternative, a certain upper bound is derived on the individual cost function. Then, in virtue of the completing-the-square technique and the Moore–Penrose pseudo inverse, such an upper bound is minimized at each time instant. Furthermore, a sufficient condition is established to guarantee the boundedness of the derived upper bound over the infinite horizon. Finally, a numerical example on the power grid is provided to verify the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

To contribute to a smart grid, the unit commitment and dispatch (UCD) is a very important problem to be revisited in the future microgrid environment. This paper studies the UCD problem by taking account of both traditional thermal units and distributed generation units while considering demand response and carbon emissions trading. First, the UCD problem is reformulated as an optimal switching problem of a hybrid system. The reformulation allows for convenient handling of time-dependent start-up cost by dynamic programming. Then, we propose a closed-loop hierarchical operation (CLHO) algorithm, which produces the optimal schedule with a closed-loop feature. Theoretical analysis shows the equivalence of the reformulation and the effectiveness of the CLHO algorithm. Finally, the closed-loop feature and the effectiveness of the proposed algorithm are further verified by simulations. The simulations also show that low emission allowance and high emissions trading price can reduce the total carbon emissions. [ABSTRACT FROM AUTHOR]

Efficient solution methods for large-scale unit commitment (UC) problems have long been an important research topic and a challenge, especially in market clearing computation. For large-scale UC, the Lagrangian relaxation methods (LR) and the mixed integer programming methods (MIP) are most widely adopted. However, LR usually suffers from slow convergence; and the computational burden of MIP is heavy when the binary variable number is large. In this paper, a variable reduction method is proposed. First, the time-coupled constraints in the original UC problem are relaxed, and many single-period UC problems (s-UC) are obtained. Second, LR is used to solve the s-UCs. Different from traditional LR with iterative subgradient method, the optimal multipliers and the approximate UC solutions of the s-UCs are obtained by solving linear programs. Third, a criterion for choosing and fixing the UC variables in the UC problem is established; hence, the number of binary variables is reduced. Finally, the UC with reduced binary variables is solved to obtain the final UC solution. The proposed method is tested on the IEEE 118-bus system and a 6484-bus system. The results show the method is very efficient and effective. [ABSTRACT FROM AUTHOR]

The predictive control method deals with the prediction of the motor behavior based on a mathematical model of the motor. This model is dependent on the motor parameters. However, these parameters may not match with their actual values due to the measurement error or they may change during the operation of the motor. All these uncertainties and model inexactitude lead to inaccurate prediction of the motor behavior and deteriorate the performance of the predictive algorithm. This paper presents an extension of the predictive current control (PCC) method to improve the prediction accuracy for a permanent-magnet synchronous motor (PMSM). The proposed strategy not only reduces the current ripple but also improves the robustness of the system against parameter uncertainties. Simulation and experimental results that confirm the good performance of the proposed method are presented. [ABSTRACT FROM PUBLISHER]

In this correspondence paper, the robust stabilization of a class of discrete-time nonlinear systems with uncertainties is investigated by using an approximate optimal control approach. The robust control problem is transformed into an optimal control problem under some proper restrictions on the bound of the uncertainties. For the purpose of dealing with the transformed optimal control, the discrete-time generalized Hamilton–Jacobi–Bellman equation is introduced and then solved using the successive approximation method with neural network implementation. In addition, a numerical simulation is included to illustrate the effectiveness of the robust control strategy. [ABSTRACT FROM PUBLISHER]

This paper presents a practical convex-optimization-based sensorless scheme for interior permanent magnet synchronous motor (IPMSM) drives. The proposed sensorless scheme simplifies the optimization process that employs only one variable as compared with two variables in the existing optimization-based methods. The reduction in variables substantially reduces the computational burden in the proposed scheme and, hence, is suitable for practical applications. Additionally, the proposed algorithm is able to estimate the rotor position regardless of the pattern of the high-frequency (HF) injected signal under low-speed conditions. This characteristic has been investigated in this paper where three typical HF injection patterns are compared. The performance of the proposed scheme is experimentally validated. [ABSTRACT FROM AUTHOR]

In this paper, we consider depth control problems of an autonomous underwater vehicle (AUV) for tracking the desired depth trajectories. Due to the unknown dynamical model and the coupling between surge and yaw motions of the AUV, the problems cannot be effectively solved by most of the model-based or proportional-integral-derivative like controllers. To this purpose, we formulate the depth control problems of the AUV as continuous-state, continuous-action Markov decision processes under unknown transition probabilities. Based on the deterministic policy gradient theorem and neural network approximation, we propose a model-free reinforcement learning (RL) algorithm that learns a state-feedback controller from sampled trajectories of the AUV. To improve the performance of the RL algorithm, we further propose a batch-learning scheme through replaying previous prioritized trajectories. We illustrate with simulations that our model-free method is even comparable to the model-based controllers. Moreover, we validate the effectiveness of the proposed RL algorithm on a seafloor data set sampled from the South China Sea. [ABSTRACT FROM AUTHOR]

A very simple sequential model predictive control (SMPC) is recently proposed to achieve high performance control of induction motor drives. By evaluating two separate cost functions for the torque and amplitude of the stator flux linkage in a cascaded way, the weighting factor in the conventional MPC is eliminated. However, it is shown in this paper that the conventional SMPC cannot achieve stable operation over the full speed range, if the cost function for the stator flux linkage is first evaluated. To solve this problem, this paper proposes a generalized SMPC (GSMPC), which is effective when any one of the two cost functions is first evaluated. Compared to the conventional SMPC, GSMPC not only eliminates the limitation on the execution order of two cost functions, but also presents less stator flux ripples and lower current total harmonic distortion with even lower average switching frequency. Furthermore, a simple field-weakening strategy is proposed and combined with GSMPC to widen the speed range by adjusting the torque and stator flux linkage reference online. Both simulation and experimental results are presented to confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]