Constant Constraint Research Articles

Greater pollination generalization is not associated with reduced constraints on corolla shape in Antillean plants.

Flowers show important structural variation as reproductive organs but the evolutionary forces underlying this diversity are still poorly understood. In animal-pollinated species, flower shape is strongly fashioned by selection imposed by pollinators, which is expected to vary according to guilds of effective pollinators. Using the Antillean subtribe Gesneriinae (Gesneriaceae), we tested the hypothesis that pollination specialists pollinated by one functional type of pollinator have maintained more similar corolla shapes through time due to more constant and stronger selection constraints compared to species with more generalist pollination strategies. Using geometric morphometrics and evolutionary models, we showed that the corolla of hummingbird specialists, bat specialists, and species with a mixed-pollination strategy (pollinated by hummingbirds and bats; thus a more generalist strategy) have distinct shapes and that these shapes have evolved under evolutionary constraints. However, we did not find support for greater disparity in corolla shape of more generalist species. This could be because the corolla shape of more generalist species in subtribe Gesneriinae, which has evolved multiple times, is finely adapted to be effectively pollinated by both bats and hummingbirds. These results suggest that ecological generalization is not necessarily associated with relaxed selection constraints.

Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

This paper proposes an adaptive neural network (NN) control approach for a direct‐current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time‐varying constraint regions, the asymmetric time‐varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time‐varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with time‐varying constraints. Using Lyapunov analysis, all signals in the closed‐loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.

Piecewise Affine H∞ Control of Half-Car Magneto-Rheological Suspension Systems

This paper presents a novel controller design for half-car magneto-rheological (MR) suspension systems. By introducing a piecewise approximation model, the nonlinear constraints of MR dampers are transformed into piecewise constant constraints. Further, by defining a piecewise affine control law, the attenuation of pitch and heave responses are realized by satisfying a given H∞ index. The control gains are derived through linear-matrix-inequality (LMI) optimization. Simulations under random and impact roads have verified the effectiveness of proposed controller on performance improvement and constraint satisfaction.

A Dynamic Programming Approach for Time‐Optimal Path Following of Robots Considering Speed Dependent Torque Constraints

AbstractTime‐optimal path following, i.e. moving a robot's end‐effector optimally along a specified geometric path, is a very important and well discussed problem in robotics. Nevertheless, most of the existing approaches concerning this topic neglect the speed dependency of torque constraints. The present paper presents a method for taking such constraints into account within a dynamic programming approach. To this end, the problem is treated in parameter space. This allows for an optimal use of existing resources. Due to the demanding constraints, precise mathematical models of the robots are indispensable. A satisfying match between model and real system can usually be achieved by parameter identification. For this purpose, it is a common way to derive the equations of motion using nominal parameters (masses, position of center of gravity, inertia and friction parameters), rewrite the equations in terms of linearly independent base parameters, and determine them with the help of measurements. Nevertheless, a parametrization of the motor torques has to be introduced in order to be able to consider their constraints within the optimization. In contrast to this, we present a general toolchain, based on the Projection Equation that directly derives the base parameter representation and furthermore the coefficients of the parametrized equations of motion. A verification in terms of a numerical example for a six‐axis industrial robot demonstrate why speed dependent torque constraints are preferable over constant torque constraints for time‐optimal robot trajectories. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Noise benefits parameter estimation in LMMSE sense

In this work, the effect of additive noise is studied in order to reduce the mean squared error (MSE) between the input parameter and its linear estimator constructed by the nonlinear system output. To improve the estimation performance, the optimal additive noise that minimizes the MSE of the noise enhanced linear minimum mean squared error (LMMSE) estimator is explored and determined. In addition, in the presence of prior information uncertainty, the estimation performances of the noise enhanced LMMSE are investigated under a constant constraint of the expected value of the output, and the corresponding algorithms are developed to find the optimal additive noise. Finally, two illustrative examples are provided to verify the theoretical results. The performance comparisons conducted between the LMMSE estimator without noise excitation and the optimal noise modified LMMSE estimator demonstrate that noise indeed improves the estimation accuracy under certain conditions.

Rank-1 constrained Multichannel Wiener Filter for speech recognition in noisy environments

Multichannel linear filters, such as the Multichannel Wiener Filter (MWF) and the Generalized Eigenvalue (GEV) beamformer are popular signal processing techniques which can improve speech recognition performance. In this paper, we present an experimental study on these linear filters in a specific speech recognition task, namely the CHiME-4 challenge, which features real recordings in multiple noisy environments. Specifically, the rank-1 MWF is employed for noise reduction and a new constant residual noise power constraint is derived which enhances the recognition performance. To fulfill the underlying rank-1 assumption, the speech covariance matrix is reconstructed based on eigenvectors or generalized eigenvectors. Then the rank-1 constrained MWF is evaluated with alternative multichannel linear filters under the same framework, which involves a Bidirectional Long Short-Term Memory (BLSTM) network for mask estimation. The proposed filter outperforms alternative ones, leading to a 40% relative Word Error Rate (WER) reduction compared with the baseline Weighted Delay and Sum (WDAS) beamformer on the real test set, and a 15% relative WER reduction compared with the GEV-BAN method. The results also suggest that the speech recognition accuracy correlates more with the Mel-frequency cepstral coefficients (MFCC) feature variance than with the noise reduction or the speech distortion level.

Sparse and constrained stochastic predictive control for networked systems

This article presents a novel class of control policies for networked control of Lyapunov-stable linear systems with bounded inputs. The control channel is assumed to have i.i.d. Bernoulli packet dropouts and the system is assumed to be affected by additive stochastic noise. Our proposed class of policies is affine in the past dropouts and saturated values of the past disturbances. We further consider a regularization term in a quadratic performance index to promote sparsity in control. We demonstrate how to augment the underlying optimization problem with a constant negative drift constraint to ensure mean-square boundedness of the closed-loop states, yielding a convex quadratic program to be solved periodically online. The states of the closed-loop plant under the receding horizon implementation of the proposed class of policies are mean square bounded for any positive bound on the control and any non-zero probability of successful transmission.

A semi-analytical solution to compositional flow in liquid-rich gas plays

Liquid-rich gas (LRG) reservoirs can exhibit complex phase and flow behavior due to gas condensation and re-vaporization and differences in phase mobilities that results in compositional variations inside the system. To date, the analysis of composition variation in liquid-rich wells has been largely limited to numerical modeling. This work uses a similarity-based analytical approach to study the in situ and flowing fluid composition of gas condensate wells producing under infinite-acting linear and radial flow under constant bottomhole pressure (BHP) condition. We propose a semi-analytical solution to the governing partial differential equations (PDEs) written in terms a compositional fluid formulation for multiphase (gas, oil and water) flow system in liquid rich gas reservoirs. The proposed solution is developed using similarity-theory (i.e. Boltzmann’s transformation) and is validated by both analytical development and numerical simulation data. Using proposed method, pressure and overall composition are solved simultaneously and rigorously without any kind of simplification or approximation, from which producing fluid composition can be fully predicted prior to the availability of field production data. Moreover, results corroborate that when LRG wells are producing under 1D linear regime – a commonly-observed flow condition for hydraulically-fractured horizontal wells completed in unconventional formations – and against a constant BHP constraint, the producing wellbore fluid composition remains constant as long as the system remains infinite acting, leading to a constant producing gas-oil ratio (GOR). For radial flow, however, producing wells team composition and GOR are shown to be time-dependent before stabilization at a nearly-constant value.

Transmit Signal Design in Colocated MIMO Radar Without Covariance Matrix Optimization

In this paper, the problem of the waveform design for colocated multiple-input multiple-output (MIMO) radars is considered in two parts. In the first part, we design transmit waveform in order to approximate the desired beampattern with low number of samples in the transmitter. Unlike the traditional waveform design methods, in our solution, waveforms are designed for a specific number of samples. Also, the constant envelope constraint that is an important practical constraint is considered. In the second part, we jointly design the transmit waveform and receive filter by a sequential algorithm, considering a priori information of target and interference angle locations. We have evaluated the performance of the proposed algorithms via numerical simulations and shown that the proposed algorithm in the first part is able to approximate the desired beampattern with low number of samples. Also, our method in the second part achieves better signal-to-interference-plus-noise ratio performance compared with the existing active sensing array methods (e.g., phased-array and correlated MIMO radars).

On Estimation of Number of Detectable Software Faults under Budget Constraint

Checking software quality and reliability of developed software system is one of the important activities for developing highly-reliable software system. In software testing phase, which is located in the final stage of software development process, software faults remaining the developed software system must be eliminated as possible as the testing manager could. Further, it is also important to develop testing plan to conduct testing activities efficiently under the certain constraints because there are some constraints, such as cost and delivery, in the actual testing phase. This paper discusses method to estimate the number of detectable faults under constant budget constraint by applying a two-dimensional Weibull type software reliability model, in which the software reliability growth process depends on testing time and testing effort factors. We finally show numerical examples of our method by using actual data.

An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

Using the Global Navigation Satellite System (GNSS) to sense three-dimensional water vapor (WV) has been intensively investigated. However, this technique still heavily relies on the a priori information. In this study, we propose an improved tomography approach based on adaptive Laplacian smoothing (ALS) and ground meteorological observations. By using the proposed approach, the troposphere tomography is less dependent on a priori information and the ALS constraints match better with the actual situation than the constant constraints. Tomography experiments in Hong Kong during a heavy rainy period and a rainless period show that the ALS method gets superior results compared with the constant Laplacian smoothing (CLS) method. By validation with radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) data, we found that the introduction of ground meteorological observations into tomography can solve the perennial problem of resolving the wet refractivity in the lower troposphere and thus significantly improve the tomography results. However, bad data quality and incompatibility of the ground meteorological observations may introduce errors into tomography results.

Near-Optimal Hybrid Processing for Massive MIMO Systems via Matrix Decomposition

For the practical implementation of massive multiple-input multiple-output (MIMO) systems, the hybrid processing (precoding/combining) structure is promising to reduce the high cost rendered by large number of RF chains of the traditional processing structure. The hybrid processing is performed through low-dimensional digital baseband processing combined with analog RF processing enabled by phase shifters. We propose to design hybrid RF and baseband precoders/combiners for multi-stream transmission in point-to-point massive MIMO systems, by directly decomposing the pre-designed unconstrained digital precoder/combiner of a large dimension. The constant amplitude constraint of analog RF processing results in the matrix decomposition problem non-convex. Based on an alternate optimization technique, the non-convex matrix decomposition problem can be decoupled into a series of convex sub-problems and effectively solved by restricting the phase increment of each entry in the RF precoder/combiner within a small vicinity of its preceding iterate. A singular value decomposition based technique is proposed to secure an initial point sufficiently close to the global solution of the original non-convex problem. Through simulation, the convergence of the alternate optimization for such a matrix decomposition based hybrid processing (MD-HP) scheme is examined, and the performance of the MD-HP scheme is demonstrated to be near-optimal.

Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint

An adaptive control-based neural network for a ${n}$ -link robot is studied and the considered robot can be transformed as a class of multi-input–multioutput systems. The position of the robot or the output of the transformed systems is constrained in a time-varying compact set. It is commonly known that the constant constraint belongs to a special case of the time-varying constraint, and thus, it can be more general for handling practical problem as compared with the existing methods for robot. The neural approximation is used to estimate the unknown functions of systems and the time-varying barrier Lyapunov function is used to overcome the violation of constraints. It can prove the stability of the closed-loop systems by using Lyapunov analysis. The feasibility of the approach is demonstrated by performing a simulation example.

Chemometric Analysis Of Inter- And Intra-Molecular Diversification Factors By A Machine Learning Simplex Approach. A Review And Research On Astragalus Saponins.

Metabolisms represent highly organized systems characterized by strong regulations satisfying the mass conservation principle. This makes a whole chemical resource to be competitively shared between several ways at both intra-and inter-molecular scales. Whole resource sharing can be statistically associated with a constant sum-unit constraint which represents the basis of simplex mixture rule. In this work, a new simplex-based simulation approach was developed to learn scaffold information on metabolic processes controlling molecular diversity from a wide set of observed chemical structures. Starting from a dataset of chemical structures classified into p clusters, a machine learning process was applied by linearly combining the p clusters j and randomly sampling a constant number (n) of molecules according to different clusters’ weights (wj/w) given by Scheffé’s mixture matrix. At the output of mixture design, molecular linear combinations lead to calculate barycentric molecules integrating the characteristics of the different weighted clusters. The N mixtures-design was iterated by bootstrap technique leading to extensive exploration of chemical variability between and within clusters. Finally, the K response matrices issued from K iterated mixture designs were averaged to calculate a smoothed matrix containing scaffold information on regulation processes responsible for molecular diversification at inter- and intra-molecular (atomic) scales. This matrix was used as a backbone for graphical analysis of positive and negative trends between atomic characteristics (chemical substitutions) at both mentioned scales. This new simplex approach was illustrated by cycloartane-based saponins of Astragalus genus by combining three desmosylation clusters characterized by relative glycosylation levels of different aglycones’ carbons.

Coverage Control of Unicycle Agents under Constant Speed Constraints

This paper studies the problem of optimal coverage control over a convex bounded region by a group of unicycle-type mobile agents with constant speeds. We further assume that the constant speeds for different agents may be non-identical. Thus, a unicycle is controlled solely via steering of its orientation in which a virtual centre is defined for each unicycle. A basic controller is proposed which asymptotically drive the virtual centres to a Centroidal Voronoi configuration, thus achieving the optimal coverage objective. At the Centroidal Voronoi configuration, each unicycle executes steady-state circular orbit about its virtual centre. To overcome a drawback of the basic controller that requires a careful selection of initial conditions, a modified switching controller is proposed which achieves a Centroidal Voronoi configuration for arbitrarily chosen initial conditions. Simulations for both the basic and modified controllers are presented for a five-unicycle group.

Evaluation of GHG Mitigation Measures in Rice Cropping and Effects of Farmer’s Characteristics: Evidence from Hubei, China

Greenhouse Gas emissions from agricultural activities, such as rice cropping, have been proven to be an important cause of climate change, with constant barriers and constraints in the implementation and promotion of mitigation measures among farmers in China. However, there has been a lack of research focusing on specific mitigation measures and their characteristics. In this paper, we applied the expert assessment and best-worst scaling method to evaluate mitigation measures in rice cropping from the perspectives of effectiveness and applicability. The results showed that no mitigation measure in rice cropping was best from both an effectiveness and applicability viewpoint. However, the study found that “reducing the use of chemical fertilizers” was the most effective one, while “applying soil testing and formulated fertilization” was the most applicable one. Additionally, the older farmers spending more time on non-agricultural jobs and farming more plots of land were more likely to believe that mitigation measures related to the management of input elements would be more applicable compared to soil and water management or some types of new science and technology. Finally, we suggested that the agricultural extension agencies should popularize input elements management such as improvement of fertilizer or seeds among older farmers, and meanwhile promote soil cultivation management and new technologies for agriculture in areas where the land is more concentrated such as plains.

Determinants of foreign-owned subsidiary performance in emerging economies

PurposeThe purpose of this study is to expand our understanding of performance determinants in foreign-owned subsidiaries (FOSs) located in an emerging economy.Design/methodology/approachA survey has been conducted of all known FOSs in Taiwan.FindingsThe results show that intra-organisational relationships and subsidiary competences are positively associated with various performance dimensions. Being located in a global city, as well as geographically distant from the home country, seem to have the opposite impact. The moderating factors of subsidiary age and authority are partially relevant.Research limitations/implicationsThis study is based on a cross-sectional sample of FOSs in Taiwan.Practical implicationsThe study shows that in an emerging market context, being strongly linked with the rest of the multinational enterprises might still be more conducive to subsidiary performance than venturing out. This is an important consideration, given the constant resource constraints for managers.Practical implicationsThis study highlights important factors that ought to be taken into consideration by policymakers to attract suitable foreign direct investment.Originality/valueThis study provides a comprehensive test of major performance determinants for FOSs located in an emerging economy.

Stability and performance analysis of linear positive systems with delays using input–output methods

ABSTRACTIt is known that input–output approaches based on scaled small-gain theorems with constant D-scalings and integral linear constraints are non-conservative for the analysis of some classes of linear positive systems interconnected with uncertain linear operators. This dramatically contrasts with the case of general linear systems with delays where input–output approaches provide, in general, sufficient conditions only. Using these results, we provide simple alternative proofs for many of the existing results on the stability of linear positive systems with discrete/distributed/neutral time-invariant/-varying delays and linear difference equations. In particular, we give a simple proof for the characterisation of diagonal Riccati stability for systems with discrete-delays and generalise this equation to other types of delay systems. The fact that all those results can be reproved in a very simple way demonstrates the importance and the efficiency of the input–output framework for the analysis of linear positive systems. The approach is also used to derive performance results evaluated in terms of the L1-, L2- and L∞-gains. It is also flexible enough to be used for design purposes.

Detachable glass microelectrodes for recording action potentials in active moving organs.

Here, we describe new detachable floating glass micropipette electrode devices that provide targeted action potential recordings in active moving organs without requiring constant mechanical constraint or pharmacological inhibition of tissue motion. The technology is based on the concept of a glass micropipette electrode that is held firmly during cell targeting and intracellular insertion, after which a 100-µg glass microelectrode, a "microdevice," is gently released to remain within the moving organ. The microdevices provide long-term recordings of action potentials, even during millimeter-scale movement of tissue in which the device is embedded. We demonstrate two different glass micropipette electrode holding and detachment designs appropriate for the heart (sharp glass microdevices for cardiac myocytes in rats, guinea pigs, and humans) and the brain (patch glass microdevices for neurons in rats). We explain how microdevices enable measurements of multiple cells within a moving organ that are typically difficult with other technologies. Using sharp microdevices, action potential duration was monitored continuously for 15 min in unconstrained perfused hearts during global ischemia-reperfusion, providing beat-to-beat measurements of changes in action potential duration. Action potentials from neurons in the hippocampus of anesthetized rats were measured with patch microdevices, which provided stable base potentials during long-term recordings. Our results demonstrate that detachable microdevices are an elegant and robust tool to record electrical activity with high temporal resolution and cellular level localization without disturbing the physiological working conditions of the organ.NEW & NOTEWORTHY Cellular action potential measurements within tissue using glass micropipette electrodes usually require tissue immobilization, potentially influencing the physiological relevance of the measurement. Here, we addressed this limitation with novel 100-µg detachable glass microelectrodes that can be precisely positioned to provide long-term measurements of action potential duration during unconstrained tissue movement.

Sound transmission loss characteristics of sandwich panels with a truss lattice core.

Sandwich panels are extensively used in constructional, naval, and aerospace structures due to the high stiffness and strength-to-weight ratios. In contrast, the sound transmission properties are adversely influenced by the low effective mass. Phase velocity matching of structural waves propagating within the panel and the incident pressure waves from the fluid medium leads to coincidence effects resulting in reduced impedance and high sound transmission. Truss-like lattice cores with porous microarchitecture and reduced inter panel connectivity offer the potential to satisfy the conflicting structural and vibroacoustic response requirements. This study combines Bloch-wave analysis and the finite element method to understand wave propagation and hence sound transmission in sandwich panels with a truss lattice core. Three dimensional coupled fluid-structure finite element simulations are conducted to compare the performance of a representative set of lattice core topologies. Potential advantages of sandwich structures with a lattice core are identified. The significance of partial band gaps is evident in the sound transmission loss characteristics of the panels studied. This work demonstrates that, even without optimization, significant enhancements in sound transmission loss performance can be achieved in truss lattice core sandwich panels compared to a traditional sandwich panel employing a honeycomb core under constant mass constraint.