Weighted Cost Function Research Articles

Variable cost model predictive control strategies for providing high‐quality power to AC microgrids

This study presents multiple schemes for improving power quality in microgrids. These schemes are based on model predictive control (MPC) systems and provide solutions to power quality challenges such as harmonic load sharing, operation mode transfer transients and simultaneous control of voltage and current quality. Some of these solutions such as the MPC-based smart impedance, variable cost event-based operation mode transition control, and droop-based hybrid voltage and current quality control are presented here for the first time. On the other hand, some solutions such as harmonic spectrum shaping are well-known techniques and are applied here for presenting a complete solution pack for solving power quality issues such as resonance. The concept of the adaptive weighting of cost function regarding the grid conditions and the required level of power quality is the core concept that is introduced in this study. After explaining each scheme, by using simulations in MATLAB/SIMULINK, its figure of merits is proved.

An optimization framework for routing protocols in VANETs: a multi-objective firefly algorithm approach

With Automobiles becoming the main form of transportation adopted in all parts of the world, it has become a necessity to develop useful applications providing safety and entertainment by harnessing the communication between the vehicles. Vehicular adhoc networks (VANET) forms the backbone for efficiently communicating among the vehicles. VANETs on the downside do not have a stable topology and has frequent network disconnections due to its high mobility. Taking all these factors into consideration, designing and implementing VANET routing protocols is a challenge. The proposed framework depends on the use of network resources to further reflect the current system condition and adjust the arrangement between continuous network topology changes and the QoS needs. It consists of three stages: The VANET scenario generator for creating network road and traffic scenarios, formulating the weighted cost function, and finally the optimization phase to identify the optimized configuration based on the weighted cost function formulated. The proposed approach (FA-OLSR) was simulated and the simulation results revealed and improved Packet Delivery Ratio, Mean Routing Load, and End-to-End Delay.

Multi-projection deep learning network for segmentation of 3D medical images

Segmentation of three-dimensional (3D) medical images using deep learning is a challenging task due to the lack of a 3D medical image dataset and their ground truth, resource memory limitations, and imbalanced dataset problem. In this paper, we propose advanced deep learning network for segmentation of 3D medical images. The proposed Multi-projection Network can preserve resource memory by applying two-dimensional (2D) kernels while still obtaining the 3D information from the image by incorporating slices from different planar projections of the 3D image to achieve good segmentation results. The proposed network uses a weighted cost function to address the imbalanced dataset problem and introduces an adaptive weight that considers the probability of each class in the image. The experimental results showed that the proposed Multi-projection Network can produce the highest sensitivity (true positive rate) compared to other architectures despite the high class imbalance in the dataset and small amount of training data.

3D Multi-Beam and Null Synthesis by Phase-Only Control for 5G Antenna Arrays

This paper presents an iterative algorithm for the synthesis of the three-dimensional (3D) radiation pattern generated by an antenna array of arbitrary geometry. The algorithm is conceived to operate in fifth-generation (5G) millimeter-wave scenarios, thus enabling the support of multi-user mobile streaming and massive peer-to-peer communications, which require the possibility to synthesize 3D patterns with wide null regions and multiple main beams. Moreover, the proposed solution adopts a phase-only control approach to reduce the complexity of the feeding network and is characterized by a low computational cost, thanks to the closed-form expressions derived to estimate the phase of each element at the generic iteration. These expressions are obtained from the minimization of a weighted cost function that includes all the necessary constraints. To finally check its versatility in a 5G environment, the developed method is validated by numerical examples involving planar and conformal arrays, considering desired patterns with different numbers of main beams and nulls.

Autotuning Technique for the Cost Function Weight Factors in Model Predictive Control for Power Electronic Interfaces

This paper presents an autotuning technique for the online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute tracking error of the control objectives and the corresponding constraints. The proposed method eliminates the need of the trial-and-error approach to determine a fixed weight factor in the cost function. The application considered is a capacitor-less static synchronous compensator based on the MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors. The result demonstrates that the proposed autotuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties in the system. The proposed capacitor-less reactive power compensator based on the autotuned MPC cost function weight factor is verified experimentally.

A new perceptually weighted cost function in deep neural network based speech enhancement systems

Speech intelligibility improvement is an important task to increase human perception in telecommunication systems and hearing aids when the speech is degraded by the background noises. Although, deep neural network (DNN) based learning architectures which use mean square error (MSE) as the cost function has been found to be very successful in speech enhancement areas, they typically attempt to enhance the speech quality by uniformly optimizing the separation of a target speech signal from a noisy observation over all frequency bands. In this work, we propose a new cost function which further focuses on speech intelligibility improvement based on a psychoacoustic model. The band-importance function, which is a principal component of speech intelligibility index (SII), has been used to determine the relative contribution to speech intelligibility provided by each frequency band in learning algorithm. In addition, we augment a signal to noise ratio (SNR) estimation to the network to improve the generalization of the method to unseen noisy conditions. The performance of the proposed MSE cost function is compared with the conventional MSE cost function in the same conditions. Our approach shows better performance in objective speech intelligibility measures such as coherence SII (CSII) and short-time objective intelligibility (STOI), while mitigating quality scores in perceptual evaluation of speech quality (PESQ) and speech distortion (SD) measure.

Levy-particle swarm optimization intelligent search-based iterative identification for nonparametric models of bilinear systems with Gaussian mixture noises

Motivated by the fact that the model-based robust identification has not been studied extensively, a novel iterative identification method is proposed for the nonparametric model of bilinear systems with Gaussian mixture noises. The proposed method is robust to the impulsive disturbances, and combines intelligent search techniques with robust estimation theories. Firstly, the block-oriented Wiener-Hammerstein model is adopted to achieve the fast modeling, and the Levy-particle swarm optimization intelligent search is exploited for the parameter estimation. Secondly, according to re-descending M-estimators, the weighted cost function is designed to suppress the influence of outliers. Thirdly, based on the Levy-PSO intelligent search and the weighted cost function, the iterative identification procedure is conducted to obtain the robust and accurate parameter estimates. Finally, the simulation tests demonstrate the effectiveness of the proposed model and the proposed method.

Co-optimization method to reduce the pattern distortion caused by polarization aberration in anamorphic EUV lithography

Extreme ultraviolet lithography is regarded as the most attractive technology to achieve 7 nm node and below. A new high-numerical-aperture anamorphic objective lens is designed to extend the single exposure resolution limit. However, the polarization aberrations (PAs) induced by the multilayer coatings on mirrors cause pattern distortions that cannot be neglected. In this paper, a source, mask, and process parameter co-optimization method is developed to compensate for the pattern distortions caused by PAs and increase the process window (PW). We first present an asymmetric source represented by the superposition of Zernike polynomials to reduce the pattern placement error (PPE). Then, a weighted cost function that incorporates the influences of PAs is innovated. Finally, a gradient-based statistical optimization method is adopted to minimize the cost function by optimizing the lithography system parameters alternately. Simulations at the 7 nm node of the 1D mask pattern indicate that for the system with a PA of marginal field, compared with our earlier work, the critical dimension error and PPE of the proposed method are reduced by 75.0% and 82.4%, respectively, and the PW is increased by 97.4%.

Minimum Swept-Path Control for Autonomous Reversing of a Tractor Semi-Trailer

This paper presents a new approach for autonomous reversing of a tractor-semitrailer vehicle called minimum swept path control (MSPC). MSPC improves the performance of previous path following control (PFC) strategies, by minimizing the maximum excursion of the vehicle, while guaranteeing acceptable path following accuracy. A linear controller is devised, combining state feedback control with an optimized preview distance. A relationship between the maximum lateral offsets of the front axle of the tractor unit and the rear axle of the last trailer and the corresponding weights in the control cost function is established, and the controller is tuned to provide the best performance compromise. Several simulations of a tractor-semitrailer case are run, and the results show that the overall swept path width is reduced by more than 40% when compared with PFC methods.

Risk-Sensitive Linear Control for Systems With Stochastic Parameters

A novel risk-sensitive (RS) control law is proposed for linear systems with stochastic system parameters. RS control laws are efficient means of handling risk in various failures in control caused by stochastic disturbances. However, stochastic parameters invoke problems, resulting controllers may become nonlinear in the state variable and incompatible with linear systems, cannot be obtained in an exact sense, and are defined only on a bounded state region. To solve these problems, this paper presents a risk-sensitive linear (RSL) control method. The important idea is that a standard RS-type cost function is converted to an expectation of a weighted cost function such that the resulting optimal controller is linear in the state. The weight is designed such that the weighted cost function preserves the characteristics of the original RS control. The designed weight allows one to derive the proposed RSL controller whose exact solution is obtained as a linear feedback law for all states. Furthermore, the RSL control law over an infinite horizon guarantees stochastic stability of the feedback system with the control law. The effectiveness of the proposed RSL control law is demonstrated by a numerical simulation.

Laplacian-based 3D mesh simplification with feature preservation

We propose a novel Laplacian-based algorithm that simplifies triangle surface meshes and can provide different preservation ratios of geometric features. Our efficient and fast algorithm uses a 3D mesh model as input and initially detects geometric features by using a Laplacian-based shape descriptor (L-descriptor). The algorithm further performs an optimized clustering approach that combines a Laplacian operator with K-means clustering algorithm to perform vertex classification. Moreover, we introduce a Laplacian weighted cost function based on L-descriptor to perform feature weighting and error statistics comparison, which are further used to change the deletion order of the model elements and preserve the saliency features. Our algorithm can provide different preservation ratios of geometric features and may be extended to handle arbitrary mesh topologies. Our experiments on a variety of 3D surface meshes demonstrate the advantages of our algorithm in terms of improving accuracy and applicability, and preserving saliency geometric features.

Mitigating the Impact of Mask Absorber Error on Lithographic Performance by Lithography System Holistic Optimization

The non-ideal mask absorber can cause an increase in critical dimension error (CDE) and decrease in process window (PW). However, the random mask absorber errors induced during mask fabricating and measuring are not considered in computational lithography. The problem cannot be neglected as the continuous scaling of lithography technology node. In this work, for the first time to our knowledge, a source, numerical aperture (NA), and process parameters co-optimization (SNPCO) method is developed to reduce the CDE induced by absorber errors and improve the PW. First, the source is represented by Zernike polynomials to balance computational burden and flexibility of source. Then a weighted cost function containing CDE and PW that incorporates the influences of absorber errors is created. Finally, a statistical optimization method is used to optimize the lithographic system parameters. Simulations of 1D mask pattern show that for the system with extreme absorber errors, the pattern errors of the proposed method are reduced by 62.1% and 58.9%, and the PWs are increased by 40.3% and 36.4%, respectively. The results illustrate that this method is effective in mitigating the CDE caused absorber errors and improving process robustness.

Driver intention based coordinate control of regenerative and plugging braking for electric vehicles with in‐wheel PMSMs

Electric vehicles have been the focus of the automotive industry in recent years. However, relatively small driving range of electric vehicles makes it not be broadly adopted in the market. Regenerative braking is one of the most effective ways to extend the endurance of electric vehicles. To sufficiently utilise the regenerative braking of electric vehicles and explore the potential of the electric motor plugging braking capability to simplify the braking system structure and reduce the cost, a new braking strategy based on the driver's braking intention and motor working characteristics is proposed. Driver's braking intention is classified as the emergency braking and the normal braking. In the case of normal braking, model predictive control (MPC) is used to express driver's braking intention. By adjusting the weight of the MPC cost function, different braking intentions can be achieved. This strategy is able to achieve as much as possible braking energy recovery without violating the driver's braking intention. In the case of the emergency braking, the sliding mode based optimal slip ratio control is adopted and it is able to obtain the shortest braking distance. In order to validate the effectiveness of the proposed approach, numerical simulations on a quarter-vehicle braking model are tested.

Trajectory optimization for accompanying satellite obstacle avoidance

This paper aims at the trajectory optimization problem of an accompanying satellite in the space station under multi-obstacles by model predictive control (MPC) with successive linearization. This optimization problem is reformulated as a mixed integer second-order cone programming (MISOCP) problem by considering the obstacles avoidance, and various physical constraints. More specifically, a novel variation weight cost function is established with the distance information of the accompanying satellite and obstacles, such that a multi-objective optimization performance is achieved by incorporating fuel consumption and time expenditure further. Through the successive linearization of the MPC scheme, the satisfaction of the multiple constraints is established. These results lead to a feasible solution with harsh constraints as well as the fast convergence to the optimal solution. The obstacles avoidance constraints are first implemented through the appropriate selection of initial values, and then solved by successive approximation with the MPC framework for the convergence of solution. Numerical simulation has been conducted to demonstrate the effectiveness and applicability of the proposed method.

A Multi-objective Optimization of Switched Reluctance Motor using a Hybrid Analytic-ANFIS Model Considering the Vibrations

Switched reluctance motors have a rugged construction which makes them suitable for many applications. But, high torque ripple, vibration, and acoustic noise are the main drawbacks of them. In this paper, the vibration is considered in optimization besides the other desired considerations. The radial force is the main origin of vibration in switched reluctance motor, and a torque-to-force ratio is introduced for considering the low vibration in optimization. Also, modeling of switched reluctance motor is somehow challenging. In this paper, a hybrid method based on analytic and adaptive-neuro-fuzzy inference system methods is presented for modeling SRM and the obtained static characteristics are used in a dynamic simulation method. Discussion on simulation results is presented at the final part of the paper which shows the lower vibrations lead to bigger air gap length; so, to prevent the deterioration of other features, an optimization should be done. A multi-objective particle swarm optimization is used to find the dominant design, and the best design is obtained by means of weighted cost functions.

Multiobjective and Interactive Genetic Algorithms for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm.

Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker's satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

Dynamic RLE-Compressed Edit Distance Tables Under General Weighted Cost Functions

Kim and Park [A dynamic edit distance table, J. Disc. Algo., 2:302–312, 2004] proposed a method (KP) based on a “dynamic edit distance table” that allows one to efficiently maintain unit cost edit distance information between two strings [Formula: see text] of length [Formula: see text] and [Formula: see text] of length [Formula: see text] when the strings can be modified by single-character edits to their left or right ends. This type of computation is useful e.g. in cyclic string comparison. KP uses linear time, [Formula: see text], to update the distance representation after each single edit. Recently Hyyrö et al. [Incremental string comparison, J. Disc. Algo., 34:2-17, 2015] presented an efficient method for maintaining the dynamic edit distance table under general weighted edit distance, running in [Formula: see text] time per single edit, where [Formula: see text] is the maximum weight of the cost function. The work noted that the [Formula: see text] space requirement, and not the running time, may be the main bottleneck in using the dynamic edit distance table. In this paper we take the first steps towards reducing the space usage of the dynamic edit distance table by RLE compressing [Formula: see text] and [Formula: see text]. Let [Formula: see text] and [Formula: see text] be the lengths of RLE compressed versions of [Formula: see text] and [Formula: see text], respectively. We propose how to store the dynamic edit distance table using [Formula: see text] space while maintaining the same time complexity as the previous methods for uncompressed strings.

A wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry

We present a new wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry (2-D WTP). First of all, the maximum value point is extracted from two-dimensional wavelet transform coefficient modulus, and the local extreme value points over 90% of maximum value are also obtained, they both constitute wavelet ridge candidates. Then, the gradient of rotate factor is introduced into the Abid’s cost function, and the logarithmic Logistic model is used to adjust and improve the cost function weights so as to obtain more reasonable value estimation. At last, the dynamic programming method is used to accurately find the optimal wavelet ridge, and the wrapped phase can be obtained by extracting the phase at the ridge. Its advantage is that, the fringe pattern with low signal-to-noise ratio can be demodulated accurately, and its noise immunity will be better. Meanwhile, only one fringe pattern is needed to projected to measured object, so dynamic three-dimensional (3-D) measurement in harsh environment can be realized. Computer simulation and experimental results show that, for the fringe pattern with noise pollution, the 3-D surface recovery accuracy by the proposed algorithm is increased. In addition, the demodulation phase accuracy of Morlet, Fan and Cauchy mother wavelets are compared.

A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks

More and more network traffic data have brought great challenge to traditional intrusion detection system. The detection performance is tightly related to selected features and classifiers, but traditional feature selection algorithms and classification algorithms can’t perform well in massive data environment. Also the raw traffic data are imbalanced, which has a serious impact on the classification results. In this paper, we propose a novel network intrusion detection model utilizing convolutional neural networks (CNNs). We use CNN to select traffic features from raw data set automatically, and we set the cost function weight coefficient of each class based on its numbers to solve the imbalanced data set problem. The model not only reduces the false alarm rate (FAR) but also improves the accuracy of the class with small numbers. To reduce the calculation cost further, we convert the raw traffic vector format into image format. We use the standard NSL-KDD data set to evaluate the performance of the proposed CNN model. The experimental results show that the accuracy, FAR, and calculation cost of the proposed model perform better than traditional standard algorithms. It is an effective and reliable solution for the intrusion detection of a massive network.

Multi-Objective Weight Optimization for Trajectory Planning of an Airplane with Structural Damage

This paper presents an integrated approach to determine and apply cost criteria weights for the multi-objective optimization (MOO) problem of emergency landing trajectory generation for an aircraft with structural damage. Cost criteria including terrain avoidance, Safety Value Index (SVI), fire cost, hydraulic and fuel costs and safe landing constraints such as touchdown heading and position, airspeed, and glide slope are defined. A potential field strategy is utilized to rapidly generate solutions based on a library of damaged airplane motion primitives including trim states and transition maneuvers between the trim conditions. As is typical, the diverse metrics compete, preventing simultaneous optimization over all objectives. This paper proposes a novel approach to translate the subjective information provided by Pareto analysis into a weighted cost function using an entropy-based weight selection method. The resultant weights reflect the subjective preferences of a decision maker in the total integrated cost metric. Simulation results demonstrate the effectiveness of weight selection based on the proposed method.