Minimum Computation Time Research Articles

Prevention of vehicle rollover after wheel lift-off using energy-based controller with proportional gain augmentation

Vehicle rollovers are the most fatal type of vehicle crashes among all road vehicle accidents. Off-road vehicles with a high centre of gravity are highly susceptible to rollovers, which can be either tripped or untripped, caused by external impacts or during high-speed manoeuvres, respectively. Currently, rollover prediction systems are used to indicate any potential wheel lift-off in real time. Subsequently, rollover prevention systems mitigate the impending wheel lift-off. In a severe near-rollover wheel lift-off scenario, the existing anti-rollover control systems are not effective in reinstating the vehicle to safety, as the designed range of operation is limited only to the near-wheel lift-off region. Even after the wheel lift-off occurs, the vehicle can be brought safely back to the ground with the timely intervention of the anti-rollover controller. This study proposes a novel energy-based anti-rollover controller with proportional gain augmentation using steering wheel input to reinstate a vehicle from a high-speed on-road near-rollover scenario with wheels on one side in the lifted-off condition. Use of an energy-based controller facilitates the least complicated implementation with minimum computation time for a severe scenario where the response time available is very small. A nonlinear underactuated inverted double pendulum on a cart model is used for predicting the dynamics of the vehicle in a near-rollover scenario. The design of the energy-based controller is done using the Lyapunov-based controller design method. An enhancement in the performance is obtained with the incorporation of an additional proportional gain controller. The behaviour of the designed anti-rollover controller is studied using the inverted double pendulum on a cart model. The effectiveness of the anti-rollover controller in a real-life scenario was studied using the co-simulation of a sophisticated four-wheeled pick-up model available in TruckSim® vehicle dynamic simulation software and the anti-rollover control system implemented in MATLAB®/Simulink® environment.

Fingerprint indoor positioning based on user orientations and minimum computation time

Indoor Positioning System (IPS) has an important role in the field of Internet of Thing. IPS works based on many existing radio frequency technologies. One of the most popular methods is WLAN Fingerprint because this technology has been installed widely inside buildings and it provides a high level of accuracy. The performance is affected by people who hold mobile devices (user) and also people around the users. This research aimed to minimize the computation time of kNN searching process. The results showed that when the value of k in kNN was greater, the computation time increased, especially when using Cityblock and Minkowski distance function. The smallest average computation time was 2.14 ms, when using Cityblock. Then the computational time for Euclidean and Chebychev were relatively stable, i.e. 2.2 ms and 2.23 ms, respectively.

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

On the comparison of three numerical methods applied to building simulation: Finite-differences, RC circuit approximation and a spectral method

Predictions of physical phenomena in buildings are carried out by using physical models formulated as a mathematical problem and solved by means of numerical methods, aiming at evaluating, for instance, the building thermal or hygrothermal performance by calculating distributions and fluxes of heat and moisture transfer. Therefore, the choice of the numerical method is crucial since it is a compromise among (i) the solution accuracy, (ii) the computational cost to obtain the solution and (iii) the complexity of the method implementation. An efficient numerical method enables to compute an accurate solution with a minimum computational run time (CPU). On that account, this article brings an investigation on the performance of three numerical methods. The first one is the standard and widely used finite-difference approach, while the second one is the so-called RC approach, which is a particular method brought to the building physics area by means of an analogy of electric circuits. The third numerical method is the spectral one, which has been recently proposed to solve nonlinear diffusive problems in building physics. The three methods are evaluated in terms of accuracy on the assessment of the dependent variable (temperature or vapor pressure) or of density of fluxes for three different cases: i) heat diffusion through a concrete slab, ii) moisture diffusion through an aerated concrete slab and iii) heat diffusion using measured temperatures as boundary conditions. Results highlight the spectral approach as the most accurate method. The RC based model with a few number of resistances does not provide accurate results for temperature and vapor pressure distributions neither to flux densities nor conduction loads.

Text Clustering Using PSO Based Dynamic Adaptive SOM for Detecting Emergent Trends

Detection and realization of new trends from corpus are achieved through Emergent Trend Detection (ETD) methods, which is a principal application of text mining. This article discusses the influence of the Particle Swarm Optimization (PSO) on Dynamic Adaptive Self Organizing Maps (DASOM) in the design of an efficient ETD scheme by optimizing the neural parameters of the network. This hybrid machine learning scheme is designed to accomplish maximum accuracy with minimum computational time. The efficiency and scalability of the proposed scheme is analyzed and compared with standard algorithms such as SOM, DASOM and Linear Regression analysis. The system is trained and tested on DBLP database, University of Trier, Germany. The superiority of hybrid DASOM algorithm over the well-known algorithms in handling high dimensional large-scale data to detect emergent trends from the corpus is established in this article.

Prediction of Large Format High Power Cell Performance Using NTG Model

Accurate prediction of cell performance using electrochemical parameters has always been a challenge in terms of computational complexity and collection of parameters for individual cell components (such as cathode, anode and electrolyte). In construction of a completely physics based model, the parameterisation is tedious and extremely time consuming. In order to save time and maintain accuracy, an alternate mathematical model which demands less experimental inputs especially from full cell alone is of interest in this study. In addition, the model which offers scope to manipulate the geometrical features such as tab positions and cell dimensions to further optimise the cell performance at minimal computational time is desirable. NTG model is a semi empirical model which predicts the transient behaviour of lithium ion batteries. In this study, two significant full cell parameters namely effective conductance and open circuit voltage are determined for a high power cell by stepped pulse experiments over a wide range of operating temperatures between -25°C and 50°C. Other parameters such as cell thermal conductivity, specific heat capacity and density are also determined. Additional heat balance equations are incorporated to simulate the thermal behaviour of cell. The model is constructed using partial differentiation equations in COMSOL and solved across a representative 3D geometry. Interestingly, the electrochemical performance curves and the cell surface temperature from simulations are quite close to that of experiments. The feasibility of potential, current density and temperature distribution in three dimensions in this model give better insights into the underlying battery physics. Although the diffusion of lithium ions is overlooked in this model for simplicity, the model offers the advantage of faster simulations for high power cells where the electrode loading is lesser.

On the Multidomain Bivariate Spectral Local Linearisation Method for Solving Systems of Nonsimilar Boundary Layer Partial Differential Equations

In this work, a novel approach for solving systems of nonsimilar boundary layer equations over a large time domain is presented. The method is a multidomain bivariate spectral local linearisation method (MD-BSLLM), Legendre-Gauss-Lobatto grid points, a local linearisation technique, and the spectral collocation method to approximate functions defined as bivariate Lagrange interpolation. The method is developed for a general system of n nonlinear partial differential equations. The use of the MD-BSLLM is demonstrated by solving a system of nonlinear partial differential equations that describe a class of nonsimilar boundary layer equations. Numerical experiments are conducted to show applicability and accuracy of the method. Grid independence tests establish the accuracy, convergence, and validity of the method. The solution for the limiting case is used to validate the accuracy of the MD-BSLLM. The proposed numerical method performs better than some existing numerical methods for solving a class of nonsimilar boundary layer equations over large time domains since it converges faster and uses few grid points to achieve accurate results. The proposed method uses minimal computation time and its accuracy does not deteriorate with an increase in time.

Lightweight and Privacy-Preserving Template Generation for Palm-Vein-Based Human Recognition

The use of human biometrics is becoming widespread and its major application is human recognition for controlling unauthorized access to both digital services and physical localities. However, the practical deployment of human biometrics for recognition poses a number of challenges, such as template storage capacity, computational requirements, and privacy of biometric information. These challenges are important considerations, in addition to performance accuracy, especially for authentication systems with limited resources. In this paper, we propose a wave atom transform (WAT)-based palm-vein recognition scheme. The scheme computes, maintains, and matches palm-vein templates with less computational complexity and less storage requirements under a secure and privacy-preserving environment. First, we extract palm-vein traits in the WAT domain, which offers sparser expansion and better capability to extract texture features. Then, the randomization and quantization are applied to the extracted features to generate a compact, privacy-preserving palm-vein template. We analyze the proposed scheme for its performance and privacy-preservation. The proposed scheme obtains equal error rates (EERs) of 1.98%, 0%, 3.05%, and 1.49% for PolyU, PUT, VERA and our palm-vein datasets, respectively. The extensive experimental results demonstrate comparable matching accuracy of the proposed scheme with a minimum template size and computational time of 280 bytes and 0.43 s, respectively.

Convergence criterion for MBIR based on the local noise-power spectrum: Theory and implementation in a framework for accelerated 3D image reconstruction with a morphological pyramid.

Model-based iterative reconstruction (MBIR) offers improved noise-resolution tradeoffs and artifact reduction in cone-beam CT compared to analytical reconstruction, but carries increased computational burden. An important consideration in minimizing computation time is reliable selection of the stopping criterion to perform the minimum number of iterations required to obtain the desired image quality. Most MBIR methods rely on a fixed number of iterations or relative metrics on image or cost-function evolution, and it would be desirable to use metrics that are more representative of the underlying image properties. A second front for reduction of computation time is the use of acceleration techniques (e.g. subsets or momentum). However, most of these techniques do not strictly guarantee convergence of the resulting MBIR method. A data-dependent analytical model of noise-power spectrum (NPS) for penalized weighted least squares (PWLS) reconstruction is proposed as an absolute metric of image properties for the fully converged volume. Distance to convergence is estimated as the root mean squared error (RMSE) between the estimated NPS and an NPS measured on a uniform region of interest (ROI) in the evolving volume. Iterations are stopped when the RMSE falls below a threshold directly related with the properties of the target image. Further acceleration was achieved by combining the spectral stopping criterion with a morphological pyramid (mPyr) in which the minimization of the PWLS cost-function is divided in a cascade of stages. The algorithm parameters (voxel size in this work) change between stages to achieve faster evolution in early stages, and a final stage with the target parameters to guarantee convergence. Transition between stages is governed by the spectral stopping criterion. The approach was evaluated on simulated CBCT data of a realistic digital abdomen phantom. Accuracy of the NPS model and evolution with time of the distance from the measured NPS was assessed in two ROIs. Performance of the spectrally-driven mPyr architecture was compared to a conventional, single stage, PWLS, and to two mPyr designs running a fixed number of iterations. The spectrally-driven mPyr achieved faster convergence, with 40% lower RMSE than the single stage PWLS, and between 10% and 20% RMSE reduction compared to other mPyr designs. The proposed spectral stopping criterion proved to be a suitable choice for a stopping rule, and, in particular, to govern mPyr stage transition.

Multiscale modeling of the elasto-plastic behavior of architectured and nanostructured Cu-Nb composite wires and comparison with neutron diffraction experiments

Nanostructured and architectured copper niobium composite wires are excellent candidates for the generation of intense pulsed magnetic fields (∼100T) as they combine both high strength and high electrical conductivity. Multi-scaled Cu-Nb wires are fabricated by accumulative drawing and bundling (a severe plastic deformation technique), leading to a multiscale, architectured, and nanostructured microstructure exhibiting a strong fiber crystallographic texture and elongated grain shape along the wire axis. This paper presents a comprehensive study of the effective elasto-plastic behavior of this composite material by using two different approaches to model the microstructural features: full-field finite elements and mean-field modeling. As the material exhibits several characteristic scales, an original hierarchical strategy is proposed based on iterative scale transition steps from the nanometric grain scale to the millimetric macro-scale. The best modeling strategy is selected to estimate reliably the effective elasto-plastic behavior of Cu-Nb wires with minimum computational time. Finally, for the first time, the models are confronted to tensile tests and in-situ neutron diffraction experimental data with a good agreement.

Efficient RES Penetration under Optimal Distributed Generation Placement Approach

In this paper, a novel version of the Optimal Distributed Generation Placement (ODGP) problem regarding the siting and sizing of Renewable Energy Sources (RESs) units is presented, called Optimal RES placement (ORESP). Power losses constitute the objective function to be minimized, subject to operational constraints. The simultaneous installation of a mix of RESs is considered and the Capacity Factor (CF) ratio is used as an aid for taking into account: (a) the geographical characteristics of the area, in which the examined Distribution Network (DN) is placed, (b) the different weather conditions, and (c) the availability of RESs, all of that at the same time, while keeping the problem complexity at minimum. The contribution of this work is that the proposed methodology bypasses the weather uncertainties and, thus, the RESs’ power generation stochasticity and provides an adequate solution with minimum computational burden and time, since the proposed CF use allows solving the problem under a straightforward way. Unified Particle Swarm Optimization (uPSO) is used for solving ODGP and ORESP. Moreover, a sensitivity analysis regarding the CFs variations is performed and finally a comparison of the proposed method with a more realistic one is performed, to consolidate further the claims of this paper. The proposed method is evaluated on RES-region-modified 33- and 118 bus systems.

Rapid interpretation of small-angle X-ray scattering data.

The fundamental aim of structural analyses in biophysics is to reveal a mutual relation between a molecule’s dynamic structure and its physiological function. Small-angle X-ray scattering (SAXS) is an experimental technique for structural characterization of macromolecules in solution and enables time-resolved analysis of conformational changes under physiological conditions. As such experiments measure spatially averaged low-resolution scattering intensities only, the sparse information obtained is not sufficient to uniquely reconstruct a three-dimensional atomistic model. Here, we integrate the information from SAXS into molecular dynamics simulations using computationally efficient native structure-based models. Dynamically fitting an initial structure towards a scattering intensity, such simulations produce atomistic models in agreement with the target data. In this way, SAXS data can be rapidly interpreted while retaining physico-chemical knowledge and sampling power of the underlying force field. We demonstrate our method’s performance using the example of three protein systems. Simulations are faster than full molecular dynamics approaches by more than two orders of magnitude and consistently achieve comparable accuracy. Computational demands are reduced sufficiently to run the simulations on commodity desktop computers instead of high-performance computing systems. These results underline that scattering-guided structure-based simulations provide a suitable framework for rapid early-stage refinement of structures towards SAXS data with particular focus on minimal computational resources and time.

Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT

Object detection and recognition is commonly used in diverse computer vision based applications and many algorithms are proposed in literature. However, application of object detection algorithms in real-time systems demand minimal computation time and comprehensive performance analysis needs to be conducted before deployment. This paper aims to conduct performance analysis of deep learning based algorithm i.e. single shot detector (SSD) in IoT based embedded devices for smart home appliances control. We have developed a smart home automation system using object detection algorithm based on model view controller (MVC) architecture deployed on Cloud of Things (CoT) i.e. Amazon Web Service (AWS) cloud for users to remotely monitor their homes. Message queuing telemetry transport (MQTT) protocol is used for communication with connected IoT devices. For load-balancing, we have proposed the concept of distributed broker to support high number of publishers and subscribers. For experimental analysis, we have connected a camera with Raspberry Pi for object detection based on deep learning algorithm (SSD) using OpenCV library in our proposed system. Experiments are conducted to evaluate the performance of object detection algorithm under varying environmental conditions by changing the light intensity level, distance of object from camera, and frame size of video. Results show that communication delay is very low (i.e. 0.2 s) as compared to processing delay in Raspberry Pi. Furthermore, changing environmental conditions have very low/insignificant impact on the processing delay of object detection algorithm i.e. average delay of 1.7 s (stdev. 0.18) and 1.8 s (stdev. 0.24) in bright and dark lighting levels, respectively. However, accuracy is deteriorated under low lighting intensity level and increased frame sizes i.e. from 95–100 to 80–85%. Selected embedded device, camera model and object detection algorithm limits the performance of object detection in real-time systems and shall be carefully selected to fulfill the requirement.

Evaluation of Load Distribution Factors on AASHTO LRFD Design Parameters of the Bridges Deck by Harmonic Analysis

Load distribution factors have a substantial role in the analysis and design of highway bridges. In this research, the effect of load distribution factors on the design parameters of bridge superstructures are studied by a numerical semi-continuum method. Three different case studies are carried out in the research to compare the accuracy and performance of the method. The objective of the first case is to control the outcomes of the method with the results of finite element method as well as AASHTO LRFD method. The second case is presented to study the effect of design parameters on load distribution factors between longitudinal girders through the three methods. Finally, a field test investigation is studied in the last case to compare all three methods with an actual field test study. It has been shown that the AASHTO LRFD method is not precise enough in comparison with the present method. The AASHTO LRFD formulas for live load distribution are quite unreliable and can give design parameters far too low or far too high. Moreover, minimum calculation time, convenient performance and high accuracy in the solution process are the other advantages of the method proposed in this research.

Intelligent Decision Making System for Solving Traveling Salesman Problems: MPSO

Multi swarm Particle swarm optimization (MPSO) is a variant of Particle Swarm Optimization (PSO) where various sub-swarms involved instead of single swarm, thereby balancing the exploitation and exploration. The progression of gbest is achieved by passing the best fitness value obtained from the child swarm and further progression of gbest is achieved by using gbest of parent swarm. TSP is an NP hard problems and its objectives is to find the minimum distance for a given cities and the constraints is to visit all the cities exactly ones to reach the final destination. In this paper we proposed MPSO approach to solve combinatorial optimization problem using BVA techniques comprising (Region_bc(Rg), Replicate_bc(Rp) and Evade_bc(Ev)), the performance of the algorithm is evaluated in terms of computation time, optimal fitness, error rate, convergence rate, convergence diversity and average convergence diversity for particle 30, the proposed techniques outperformance well with minimum computation time for optimal fitness value and error rate. In future we plan to implement the proposed technique in cloud computing environment for job scheduling problems.

Investigation of acoustic scattering from a pair soundproof spheres under external influence

A mathematical model extension is presented and numerical studies were made for the problem of acoustic scattering from two soundproof spheres (the case of hard spheres) with an arbitrary acoustic impedance under the action a spherical wave from a monopole radiation source arbitrarily located in space. The case of two spheres is of practical interest, since, on the one hand, the scattered fields from the spheres interact with each other, and on the other hand, the interaction is simple enough for it to be studied in detail. When solving the Helmholtz equations, a numerical technique based on the fast multipole method is used, which allows to achieve high accuracy of the results obtained with minimal computer time. The testing of the algorithm was carried out on the basis of the known data (from the literature) of the response on the surface of one of the spheres in the case when the axis connecting the monopole radiation source and the center of the first sphere is perpendicular to the axis connecting the centers of the two spheres. The pressure distribution around the spheres is investigated for different values of the distance between the centers of the spheres and the arbitrary location of the monopole radiation source in space. It is shown that with certain parameters of the system, the presence of a second sphere can lead to the appearance of an increase or decrease zone of pressure. The obtained results will further allow generalizations of the mathematical model to the cases of acoustic scattering from a pair of sound-permeable spheres (cases of gas bubbles or liquid droplets) and many spheres (both coaxially and arbitrarily arranged in space), and can also be used for test calculations during verification numerical solution of these generalized problems.

A cohesive XFEM model for simulating fatigue crack growth under mixed‐mode loading and overloading

SummaryStructures are subjected to cyclic loads that can vary in direction and magnitude, causing constant amplitude mode I simulations to be too simplistic. This study presents a new approach for fatigue crack propagation in ductile materials that can capture mixed‐mode loading and overloading. The extended finite element method is used to deal with arbitrary crack paths. Furthermore, adaptive meshing is applied to minimize computation time. A fracture process zone ahead of the physical crack tip is represented by means of cohesive tractions from which the energy release rate, and thus the stress intensity factor can be extracted for an elastic‐plastic material. The approach is therefore compatible with the Paris equation, which is an empirical relation to compute the fatigue crack growth rate. Two different models to compute the cohesive tractions are compared. First, a cohesive zone model with a static cohesive law is used. The second model is based on the interfacial thick level set method in which tractions follow from a given damage profile. Both models show good agreement with a mode I analytical relation and a mixed‐mode experiment. Furthermore, it is shown that the presented models can capture crack growth retardation as a result of an overload.

Optimal Unit Commitment by Considering High Penetration of Renewable Energy and Ramp Rate of Thermal Units-A case study in Taiwan

When large amounts of wind power and solar photovoltaic (PV) power are integrated into an independent power grid, the intermittent renewable energy destabilizes power output. Therefore, this study explored the unit commitment (UC) optimization problem; the ramp rate was applied to solve problems with 30 and 10 min of power shortage. The data of actual unit parameters were provided by the Taiwan Power Company. The advanced priority list method was used together with a combination of a generalized Lagrangian relaxation algorithm and a random feasible directions algorithm to solve a large-scale nonlinear mixed-integer programming UC problem to avoid local and infeasible solutions. The results showed that the proposed algorithm was superior to improved particle swarm optimization (IPSO) and simulated annealing (SA) in terms of the minimization of computation time and power generation cost. The proposed method and UC results can be effective information for unit dispatch by power companies to reduce the investment costs of power grids and the possibility of renewable energy being disconnected from the power system. Thus, the proposed method can increase the flexibility of unit dispatch and the proportion of renewable energy in power generation.

Extinction dynamics of spiral defect chaos.

Spatially extended excitable systems can exhibit spiral defect chaos (SDC) during which spiral waves continuously form and disappear. To address how this dynamical state terminates using simulations can be computationally challenging, especially for large systems. To circumvent this limitation, we treat the number of spiral waves as a stochastic population with a corresponding birth-death equation and use techniques from statistical physics to determine the mean episode duration of SDC. Motivated by cardiac fibrillation, during which the heart's electrical activity becomes disorganized and shows fragmenting spiral waves, we use generic models of cardiac electrophysiology. We show that the duration can be computed in minimal computational time and that it depends exponentially on domain size. Therefore, the approach can result in efficient and accurate predictions of mean episode duration which may be extended to more complex geometries and models.

Evolutionary correlated gravitational search algorithm (ECGS) with genetic optimized Hopfield neural network (GHNN) – A hybrid expert system for diagnosis of diabetes

In worldwide 415 million of peoples are affected by diabetics in the year of 2015, that is increased from the year of 2012. Based on the survey, it clearly shows the diabetics are one of the dangerous diseases because it leads to create several risk of early death. Due to the seriousness of the diabetic, it has been detected in early stage by creating expert system. During this process, the expert system has several issues such as accuracy of prediction due to the huge dimension of the diabetic feature that reduce the entire efficiency of the system. So, in this paper introduced the evolutionary correlated gravitational search algorithm (ECGS) for selecting the optimized features. The introduced method analyzes each diabetic feature according to the correlation and mutual information is selected with minimum computation time and cost. The selected features are processed by genetic optimized Hopfield neural network (GHNN) for predicting the diabetic related features effectively. Then the efficiency of the system is implemented using MATLAB tool that utilizes the Pima Indian Diabetic Dataset for analyzing the efficiency of introduced diabetic expert system. The efficiency of the system is evaluated in terms of using mean square error rate, F-measurer, accuracy, confusion matrix and ROC curve.