EMERS: a tree matching–based performance evaluation of mathematical expression recognition systems

Performance evaluation of mathematical expression recognition systems is attempted. The proposed method assumes expressions (input as well as recognition output) are coded following MathML or T_EX/LaT_EX (which also gets converted into MathML) format. Since any MathML representation follows a tree structure, evaluation of performance has been modeled as a tree-matching problem. The tree corresponding to the expression generated by the recognizer is compared with the groundtruthed one by comparing the corresponding Euler strings. The changes required to convert the tree corresponding to the expression generated by the recognizer into the groundtruthed one are noted. The number of changes required to make such a conversion is basically the distance between the trees. This distance gives the performance measure for the system under testing. The proposed algorithm also pinpoints the positions of the changes in the output MathML file. Testing of the proposed evaluation method considers a set of example groundtruthed expressions and their corresponding recognized results produced by an expression recognition system.     

Blood typing for families: a novel hybrid human–computer application

Crowdsourcing is a new approach which obtains information or input for a particular task by enlisting the services of the crowd. In recent crowdsourcing applications, the hybrid human–computer approach has been widely studied, to take advantage of both human beings and computers. In this paper, we propose a novel such application: blood typing for people in a family. We propose the BloodTyping method. It selects some members to take medical blood type tests, and to determine other family members' blood types, based on the inheritance rules. The aim is to reduce the number of medical tests, and thus, lower the cost. We extract rules for both +induction and ? induction. The former is to predict children's blood types from parents', and the latter is to backward-induce a parent's blood type, given those of children and the other parent. Different combinations of blood types can induce different results: some may be an exact blood type, while others are composed of several blood types. Our method is optimised by conducting the cases which generate exact blood types first. The order is guided by extra-information via crowdsourcing, including the distribution of blood types with respect to the birthplace, and the personality, which may indicate some specific blood types. Taking a family with two parents and all children as a basic unit, the algorithm can be conducted simultaneously among different families in a decentralised way. The simulation results show that BloodTyping can significantly reduce the required number of blood tests.     

Land cover change detection with a cross‐correlogram spectral matching algorithm

Timely extraction of reliable land cover change information is increasingly needed at a wide continuum of scales. Few methods developed from previous studies have proved to be robust when noise, changes in atmospheric and illumination conditions, and other scene‐ and sensor‐dependent variables are present in the multitemporal images. In this study, we developed a new method based on cross‐correlogram spectral matching (CCSM) with the aim of identifying interannual land cover changes from time‐series Normalized Difference Vegetation Index (NDVI) data. In addition, a new change index is proposed with integration of two parameters that are measured from the cross‐correlogram: the root mean square (RMS) and (1?R _max), where R _max is the maximum correlation coefficient in a correlogram. Subsequently, a method was proposed to derive the optimal threshold for judging ‘change’ or ‘non‐change’ with the acquired change index. A pilot study was carried out using SPOT VGT‐S images acquired in 1998 and 2000 at Xianghai Park in Jilin Province. The results indicate that CCSM is superior to a traditional Change Vector Analysis (CVA) when noise is present with the data. Because of an error associated with the ground truthing data, a more comprehensive assessment of the developed method is still in process using better ground truthing data and images at a larger time interval. It is worth noting that this method can be applied not only to NDVI datasets but also to other index datasets reflecting surface conditions sampled at different time intervals. In addition, it can be applied to datasets from different satellites without the need to normalize sensor differences.     

Identification of EIV models with coloured input–output noise: combining PEM and covariance matching method

In this paper, a novel identification method for discrete-time linear systems when input–output observations are contaminated by coloured noise (errors-in-variables models) is proposed. To develop the new approach, modified prediction error and covariance matching methods are utilised. It is proved that the proposed approach leads to a consistent estimation. System identification through the proposed approach entails the existence of a flat frequency interval in power spectra of input and ratio of noise-free input to input signals which is a somewhat mild assumption. Two Monte Carlo simulations are provided to explain the efficiency, numerical complexity and the application of the proposed method.     

Study on microchannel flows with a sudden contraction–expansion at a wide range of Knudsen number using lattice Boltzmann method

Numerical simulations have been performed on the pressure-driven rarefied flow through channels with a sudden contraction–expansion of 2:1:2 using isothermal two and three-dimensional lattice Boltzmann method (LBM). In the LBM, a Bosanquet-type effective viscosity and a modified second-order slip boundary condition are used to account for the rarefaction effect on gas viscosity to cover the slip and transition flow regimes, that is, a wider range of Knudsen number. Firstly, the in-house LBM code is verified by comparing the computed pressure distribution and flow pattern with experimental ones measured by others. The verified code is then used to study the effects of the outlet Knudsen number Kn _o , driving pressure ratio P _i /P _o , and Reynolds number Re, respectively, varied in the ranges of 0.001–1.0, 1.15–5.0, and 0.02–120, on the pressure distributions and flow patterns as well as to document the differences between continuum and rarefied flows. Results are discussed in terms of the distributions of local pressure, Knudsen number, centerline velocity, and Mach number. The variations of flow patterns and vortex length with Kn _o and Re are also documented. Moreover, a critical Knudsen number is identified to be Kn _oc  = 0.1 below and above which the behaviors of nonlinear pressure profile and velocity distribution and the variations of vortex length with Re upstream and downstream of constriction are different from those of continuum flows.     

Generalised Hammerstein–Wiener system estimation and a benchmark application

This paper examines the use of a so-called “generalised Hammerstein–Wiener” model structure that is formed as the concatenation of an arbitrary number of Hammerstein systems. The latter are taken here to be memoryless non-linearities followed by linear time invariant dynamics. Hammerstein, Wiener, Hammerstein–Wiener and Wiener–Hammerstein models are all special cases of this structure. The parameter estimation of this model is investigated using a standard prediction error criterion coupled with a robust gradient based search algorithm. This approach is profiled using a Wiener–Hammerstein Benchmark example, which illustrates it to be effective and, via Monte-Carlo simulation, relatively robust against capture in local minima.     

The model matching problem for a class of polynomial non-linear discrete input–output systems with cross-products. An algorithmic approach

In this paper we examine the model matching problem that concerns non-linear input–output discrete systems, containing products among delays of input and output signals, through a special factorisation. The algebraic framework of δε-operators and the star-product that we adopt describe these systems. Moreover, a certain procedure that resembles the Euclidean division allows us to discover the linear factors of those systems, with respect to the above mentioned operations. The entire approach is symbolically algorithmic and involves the use of suitable software.     

Structure and stability analysis of a Takagi–Sugeno fuzzy PI controller with application to tissue hyperthermia therapy

 In this paper, we first reveal the analytical structure of a simple Takagi–Sugeno (TS) fuzzy PI controller relative to the linear PI controller. The fuzzy controller consists of two linear input fuzzy sets, four TS fuzzy rules with linear consequent, Zadeh fuzzy logic AND and the centroid defuzzifier. We prove that the fuzzy controller is actually a nonlinear PI controller with the gains changing with process output. Utilizing the well-known small Gain Theorem in control theory, we then derive sufficient conditions for global stability of the fuzzy control systems involving the TS fuzzy PI controller. Finally, as an application demonstration, we apply the fuzzy PI controller to control issue temperature, in computer simulation, during hyperthermia therapy. The relationship between heat energy and tissue temperature is represented by a linear time-varying model with a time delay. The sufficient conditions for global stability are used to design a stable fuzzy control system. Our simulation results show that the fuzzy PI control system achieves satisfactory temperature control performance. The control system is robust and stable even when the model parameters are changed suddenly and significantly.     

SDMSim: A manufacturing service supply–demand matching simulator under cloud environment

Nowadays, with the introduction and application of new information technologies in manufacturing, various advanced manufacturing modes and national strategies have been put forward and paid more and more attention, such as Industry 4.0, Industrial Internet, Cyber-Physical System or Cyber Manufacturing, Made in China 2025, Internet Plus Manufacturing, Cloud Manufacturing, etc. For these modes and strategies, how to realize the effective and intelligent supply–demand matching (SDM) of various manufacturing resources and capabilities (MR&C) in the form of service is one of the common issues and aims. In order to provide a uniformed research platform for related researchers both in academic and industry, the concept of manufacturing service SDM simulator (SDMSim) is proposed in this paper. A hypernetwork based architecture for the simulator is designed, as well as its seven key functions and subsystems, including manufacturing service management, manufacturing task management, manufacturing service SDM hypernetwork, manufacturing service SDM problem formulation and configuration, matching and scheduling algorithms/strategies selection and design, statistical analysis, and visualization. It illustrates that SDMSim has the potential to serve the users of manufacturing service provider, manufacturing service consumer, manufacturing service operator in the field of SoM, as well as the related researchers.     

Towards a parallel component in a GPU–CUDA environment: a case study with the L-BFGS Harwell routine

Modern graphics processing units (GPUs) have been at the leading edge of increasing parallelism over the last 10 years. This fact has encouraged the use of GPUs in a broader range of applications, where developers are required to lever age this technology with new programming models which ease the task of writing programs to run efficiently on GPUs. In this paper, we discuss the main guidelines to assist the developer when porting sequential scientific code on modern GPUs. These guidelines were carried out by porting the L-BFGS, the (Limited memory-) BFGS algorithm for large scale optimization, available as Harwell routine VA15. The specific interest in the L-BFGS algorithm arises from the fact that this is the computational module with the longest running time of a Oceanographic Data Assimilation application software, on which some of the authors are working.     

Leader–follower synchronisation for networked Lagrangian systems with uncertainties: a learning approach

This article addresses a leader–follower synchronisation problem of networked Lagrangian systems with uncertainties by an iterative learning control approach. The inherent properties of the systems are fully utilised in the controller design, and a directed acyclic graph is sufficient for communication among subsystems. The developed controller contains a proportional-plus-derivative (PD) term and two learning terms. The PD term drives the tracking error to zero, one learning term compensates for the model uncertainties, and the other one is used for disturbance rejection. It is shown that the synchronisation task can be achieved by the proposed controller, and all internal signals are either bounded or norm bounded. The theoretical results are supported by a numerical study.     

New training strategies for neural networks with application to quaternary Al–Mg–Sc–Cr alloy design problems

This study concerns the training of a neural network in multiple stages considering minimization of errors from multiple data/pattern resources. The paper proposed a dual stage multi-resource data training scheme using multi-objective genetic algorithm. The training scheme has been used for the design and development of efficient neural network model focusing on missing, but most informative domains of the data set by means of introducing only a few patterns from missing domain treated separately during the later stage of training. The trained model has been used to design a quaternary Al–Mg–Cr–Sc alloy system, from the information subsets of binary Al–Cr and the ternary Al–Mg–Sc alloys. The validity of the proposed algorithm has been discussed in light of the evolution of the ageing characteristics of the new aluminium alloy system.     

Multiple classifier system for EEG signal classification with application to brain–computer interfaces

In this paper, we demonstrate the use of a multiple classifier system for classification of electroencephalogram (EEG) signals. The main purpose of this paper is to apply several approaches to classify motor imageries originating from the brain in a more robust manner. For this study, dataset II from BCI competition III was used. To extract features from the brain signal, discrete wavelet transform decomposition was used. Then, several classic classifiers were implemented to be utilized in the multiple classifier system, which outperforms the reported results of other proposed methods on the dataset. Also, a variety of classifier combination methods along with genetic algorithm feature selection were evaluated and compared in order to diminish classification error. Our results suggest that an ensemble system can be employed to boost EEG classification accuracy.     

Projection gradient method for energy functional minimization with a constraint and its application to computing the ground state of spin–orbit-coupled Bose–Einstein condensates

In this study, we propose a projection gradient method for energy functional minimization with a constraint, which we use to compute the ground state of spin–orbit-coupled Bose–Einstein condensates at extremely low temperatures. The method has the advantage that it maintains the constraint when evolving a gradient flow to find the energy functional minimization under a constraint. The original gradient projection method for energy functional minimization under a constraint only considers an energy functional with real functions as variables. Thus, we extend it to consider complex functions as independent variables. We apply the newly proposed method to study the ground state solution of spin–orbit-coupled pseudo-spin 1/2 Bose–Einstein condensates. Detailed numerical results demonstrate the effectiveness of our method. Using this method, we found various types of ground state structures of spin–orbit coupled Bose–Einstein condensates.     

H ∞ filtering for singular bilinear systems with application to a single-link flexible-joint robot

In this paper, we consider the H _∞ filters design for singular bilinear systems. The approach is based on the parameterized solution of a set of constrained Sylvester equations. The exponential convergence and l ₂ gain attenuation problems are solved by using the bounded real lemma, which leads to linear matrix inequalities (LMI) formulation. Finally, a detailed design procedure is given for the estimation of the states of a flexible joint robot, which demonstrates the effectiveness of the proposed method.     

Numerical investigation of a flexible slider–crank mechanism with multijoints with clearance

This paper aims to study the dynamic behavior of a slider–crank mechanism with flexible components and a multijoint clearance. A numerical investigation was developed for this objective. The mechanism model used for the simulation tests has been performed under MSC ADAMS software using the contact force under the “Impact-function” library. The obtained results illustrate that the mechanism performance is more significantly influenced with multiple joints with clearance. Three contact modes are involved: (i) a continuous contact motion, (ii) a free motion, and (iii) an impact motion. Numerical outcomes prove that the clearance dimension and location have a determinant effect on the slider responses precision. The mechanism reliability decay and a random overall behavior govern the dynamic response along the free flight mode.     

Anisotropic mesh adaptation for the generalized Ambrosio–Tortorelli functional with application to brittle fracture

Quasi-static crack propagation in brittle materials is modeled via the Ambrosio–Tortorelli approximation. The crack is modeled by a smooth phase-field, defined on the whole computational domain. Since the crack is confined to a thin layer, the employment of anisotropic adapted grids is shown to be a really effective tool in containing computational costs. We extend the anisotropic error analysis, applied to the classical Ambrosio–Tortorelli approximation by Artina et al., to the generalized Ambrosio–Tortorelli functional, where a unified framework for several elasticity laws is dealt with as well as a non-convex fracture energy can be accommodated. After deriving an anisotropic a posteriori error estimator, we devise an algorithm which alternates optimization and mesh adaptation. Both anti-plane and plane-strain configurations are numerically checked.