Computation Time Research Articles

AN EFFICIENT ITERATIVE METHOD TO SIMULATE THE VARYING STATIC RECEPTANCE OF A THIN-WALLED WORKPIECE

This paper introduces an accelerated iterative Conjugate Gradient method (CG-method) for simulating the varying static receptance of a thin-walled workpiece. The proposed approach enhances computational efficiency by computing an optimal initial displacement guess for a given cutter location. This guess is based on the previously calculated displacement vector from the preceding cutter location, serving as the start iteration vector for the CG-method. The validation of efficiency gains through this method is demonstrated by a significant reduction in computation time for a numerical example. Moreover, to ensure the accuracy of the accelerated iterative CG-method, comparisons are made with the Cholesky method. The results confirm the precision of the proposed approach.

Evaluation of sequential feature selection in improving the K-nearest neighbor classifier for diabetes prediction

The K-nearest neighbor (KNN) classifier employs distance metrics to measure the distance between the test instance and the samples used in training. With smaller samples, the KNN classifier achieves higher accuracy with low computational time. However, computing the distance between the test instance and all training samples to determine the class of the test instance requires higher computational time for a high-dimensional dataset. This research employs sequential feature selection (SFS) to select the optimal feature for diabetes prediction while reducing the computational time complexity of the KNN classifier. The KNN classifier showed effectiveness with an accuracy rate of 84.41% with nine features. The performance of the KNN improves by 2.6% when trained on the optimal features selected with the SFS. The result revealed glucose level, blood pressure (BP), skin thickness (ST), diabetes pedigree function (DPF), age, and body mass index (BMI) as the most representative features in diabetes prediction. The KNN classifier gives higher accuracy with these features. However, insulin and the number of times a woman is pregnant do not show a significant effect on the KNN classifier.

Development of an efficient vehicle-to-grid method for massive electric vehicle aggregation

The growing adoption of renewable energy and electric vehicles (EVs) has contributed to environmental sustainability; nevertheless, integration of these products into the power grid has become complex owing to their unpredictable nature and variable energy demands. A significant challenge lies in the realization of large-scale, coordinated control of EVs to serve as an alternative to traditional energy storage systems. This challenge is underscored by the complexity of optimization in large-scale cooperative control problems and the difficulty in reducing such problems to an easily manageable and practical level in real-world application. In response to these challenges, a practical mechanism for the integration of EVs into a vehicle-to-grid concept is proposed in this study. In this approach, the constraints involved in merging multiple EVs into a fictitious clustered energy storage unit, which are often neglected, are given renewed focus. An iterative multi-stage optimization method is introduced that includes an EV aggregation clustering model, multi-tier optimization model, and recursive framework. Here, marked efficacy for larger EV fleets is demonstrated for this method, providing optimal charging and discharging schedules for each vehicle while a high degree of precision is maintained. With the proposed technique, validated through numerous case studies using historical data, the global optimum solution is largely approximated, with a marginal deviation of 4 %. In addition, robustness of the model is demonstrated under varying pricing scenarios, with a computation time that increases in a linear manner, rather than exponentially, as the number of EVs increases. Meanwhile, compared with conventional methods, the technique proposed in this study has the capacity to fulfill the charging demands of all users with reduced charging expense, demonstrating the high precision and efficacy of the technique.

User authentication using gait and enhanced attribute-based encryption: a case of smart home

With the increasing popularity of the internet of things (IoT) application such as smart home, more data is being collected, and subsequently, concerns about preserving the privacy and confidentiality of these data are growing. When intruders attack and get control of smart home devices, privacy is compromised. Attribute-based encryption (ABE) is a new technique proposed to solve the data privacy issue in smart homes. However, ABE involves high computational cost, and the length of its ciphertext/private key increases linearly with the number of attributes, thus limiting the usage of ABE. This study proposes an enhanced ABE that utilises gait profile. By combining lesser number of attributes and generating a profiling attribute that utilises gait, the proposed technique solves two issues: computational cost and one-to-one encryption. Based on experiment conducted, computational time has been reduced by 55.27% with nine static attributes and one profile attribute. Thus, enhanced ABE is better in terms of computational time.

Automated feature selection using improved migrating birds optimization for enhanced medical diagnosis

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental Machine Learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms.

From RNN techniques to pre-trained models: emphasis on the use in Arabic machine translation

<p>Neural Machine Translation (NMT) has gained increasing attention in recent years due to its promising performance over conventional approaches such as Statistical Machine Translation. Nevertheless, when applied to languages with different structures, such as the pair (English, Arabic) that interests us in this work, its efficiency is degraded.<br />Alternatively, the use of unsupervised pre-training of large neural models has recently made a significant leap forward in the field of Natural Language Processing (NLP). By warm starting from published checkpoints, NLP practitioners have been able to push the state-of-the-art boundaries on multiple benchmarks while simultaneously saving considerable computational time. The emphasis so far has been mainly on natural language understanding problems. In this paper, we show the efficacy of pre-trained checkpoints for Arabic Machine Translation. We have evolved a transformer-based sequence-to-sequence model which is compatible with pre-trained checkpoints publicly available for Arabic Bidirectional Encoder Representations from Transformers (AraBERT) and Arabic Generative Pre-trained Transformer (AraGPT) and conducted a thorough empirical study on the usefulness of initializing our Arabic MT model, both encoder and decoder, with these checkpoints. Our models yield new state-of-the-art results in Arabic MT.</p>

Media use and attitudes towards screens in children aged 36-72 months; a cross-sectional, descriptive study.

Introduction: The age at which children are introduced to media devices is becoming increasingly earlier. Studies regarding the media habits of young children have gained importance. The aim of the study was to describe the screen media habits (age of introduction media, time spend with screen, popular choices) of preschool children and to explore their relationship with household characteristics. Population and methods: Cross-sectional descriptive study; the parents of children aged 36-72 months who attended childcare centers in Kayseri, a central Anatolian city in Türkiye, completed a questionnaire on media habits of families and children. Results: There were 858 questionaires included. The proportion of children using screen media more than 2 h/day was 28%; 36% of children were introduced to media devices before the age of two. The most frequently used media tool was television (95%) and the program watched was cartoons for TV (86.7%). Children of highly educated parents had shorter TV, computer and smartphone screen time (p = 0.012, p = 0.007, p <0.01 for mother and p = 0.049, p = 0.032, p = 0.04 for father respectively). Introducing books in the first 6 months was associated with shorter daily screen time (p = 0.011, p = 0.009, p = 0.002 for TV, computer and smartphone ,respectively). Parent's time spent on TV was positively correlated with children's time spent on TV (p <0.05, r = 0.354). Conclusion: Parents' education levels, parents' screen time and introducing book in early age was related to children's media habits. Comprehensive studies are needed to explain this relationship more clearly.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles (UAVs) have been used for many applications including military, construction, image and video mapping, medical, search and rescue, wireless communications, and aerial surveillance. One key research topic involving UAVs is pose estimation in autonomous navigation. A standard procedure for this process for UAVs is to combine inertial navigation system (INS) sensor information with the global navigation satellite system (GNSS) signal. However, some factors can interfere with the GNSS signal, such as natural phenomena (ionospheric scintillation) or malicious attacks (jamming or spoofing). One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images. But a high effort is required for image edge extraction. In this paper, a support vector regression (SVR) model is proposed to reduce the computational load and processing time for image edge detection. The dynamic partial reconfiguration (DPR) of part of the SVR datapath was implementated to accelerate the process, reduce the area, and analyze its granularity aspect by increasing the grain size of the reconfigurable region. Results show that the implementation in hardware is 68 times faster than that in software. This kind of architecure with DPR also facilitates the low power consumption of 4 mW, leading to a reduction of 57 % compared with that without DPR, which is also the lowest power consumption compared with other machine learning (ML) hardware implementations. Besides, the circuitry area is 41 times smaller. SVR with Gaussian kernel showed a success rate of 99.18 % and minimum square error of 0.0146 for testing with the planning trajectory. This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application, thus contributing to lower power consumption, smaller hardware area, and shorter execution time.

Optimal economic environmental power dispatch by using artificial bee colony algorithm

&lt;span&gt;Today, most power plants worldwide use fossil fuels such as natural gas, coal, and oil as the primary resource for energy reproduction primarily. The new term for economic environmental power dispatch (EEPD) problems is on the minimum total cost of the generator and fossil fuel emissions to address atmosphere pollution. Thus, the significant objective functions are identified to minimize the cost of generation, most minor emission pollutants, and lowest system losses individually. As an alternative, an Artificial Bee Colony (ABC) swarming algorithm is applied to solve the EEPD problem separately in the power systems on both standard IEEE 26 bus system and IEEE 57 bus system using a MATLAB programming environment. The performance of the introduced algorithm is measured based on simple mathematical analysis such as a simple deviation and its percentage from the obtained results. From the mathematical measurement, the ABC algorithm showed an improvement on each identified single objective function as compared with the gradient approach of using the Newton Raphson method in a short computational time.&lt;/span&gt;

TQU-HG dataset and comparative study for hand gesture recognition of RGB-based images using deep learning

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

3D Hybrid modeling for the ultrasonic phased array inspection of porosity in heavy plates: Simulation and experimental validation

Quantitative NDT modeling and simulation tools are important in design and fabrication of ultrasonic phased array probes with optimum characteristics and reduce the number of experimental efforts and development time. In order to achieve high accuracy (Amplitude deviation &lt; 1 dB) with a low computational time in the simulation, the modeling tools require not only the high-frequency approximation of the ultrasonic wave propagation but also the high-precision electro-acoustic coupling model. This paper describes a computationally efficient 3D time-dependent hybrid model for the simulation of ultrasonic phased array inspection of porosity in heavy plates. This method combines the results of both 3D finite element analysis (PZFLex-FEA) and the semi-analytical ultrasonic simulation “Ray-Tracing”. As a first step, we demonstrate the FEA modeling of an ultrasonic phased array probe, which includes the full 1-3 piezocomposite design with the arrangement of array elements, conducting electrodes, multi-phase backing material, acoustical matching layer and electrical circuit of a 50 Ohm coaxial cable. In the current FEA simulation, we considered the acoustical and electrical cross-talk between the adjacent elements of an ultrasonic array sensor. In the second step, the FEA-simulated ultrasonic pulse-echo signal and its frequency spectrum of an array element in the farfield region are further used as the reference input signal in the CIVA-UT software to simulate the imaging of porosity in heavy plates, quantitatively. The design of the 3D wedge-geometry of the application specific array sensor is modeled and optimized using the CIVA raytracing and multi-phase backing models. The real excitation pulse of the industrial ultrasonic testing equipment “ROMIS (ROSEN Modular Inspection System)” is also included in the simulation to achieve high accuracy in the current hybrid modeling. The dependency of the detectability of the porosity on the frequency of the array sensor and the active element group so-called “Virtual Probe” are quantitatively analyzed. Finally, the 3D hybrid model simulated Time Corrected Gain (TCG) curves of the porosity like defects in the reference steel plates are compared with the real experiments and a good quantitative agreement is achieved.

Analysis of magnetic saturation effects in the squirrel cage induction generators

This work concerns an investigation on the analysis of the magnetic saturation effects in the three-phase squirrel cage induction generator which is considered as a main device in wind energy conversion systems. The magnetic saturation is considered as an important factor causing destructive effects on power qualities such as harmonics and distortion. Several approaches have been presented in the literature for the modeling of the electric machines considering magnetic saturation. The widely used and precise approach is the finite elements method which is particularly characterized by its high computational time. The novelty in this paper is that a state model has been developed for the healthy conditions of the three-phase squirrel cage induction generators considering the experimental variation of the magnetizing inductance in terms of the magnetizing current. Simulation and experimental tests are provided to extract some important signatures on stator voltages and currents. It will be deduced that the spectrum analysis of stator currents contains useful information on magnetic saturation. Experimental and theoretical results illustrate the consistency of this approach for the modeling and analysis of the squirrel cage induction generators considering the magnetic saturation.

A fast analysis algorithm for structural vibration modal sensitivity

Vibration modal sensitivity analysis has a wide range of applications in structural optimization design, model correction, fault detection, and reliability analysis. The existing methods for calculating modal sensitivity mainly include modal superposition method, Nelson’s method, and some improved algorithms derived from them. However, the existing algorithms have the drawback of low computational efficiency when applied to modal sensitivity analysis of the large-scale structures. In view of this, the objective of this work is to develop a fast modal sensitivity analysis method that can more efficiently calculate the modal sensitivity of the large structures with thousands of degrees of freedom. This new sensitivity analysis technique integrates Sherman-Morrison-Woodbury formula, subspace iteration and forward-difference to achieve the goal of fast calculation. The innovations of the proposed method mainly include the following aspects. Firstly, the Sherman-Morrison-Woodbury formula is used to quickly calculate the perturbed flexibility matrix due to the minor modification in the structure. Then, the perturbed flexibility matrix is introduced into the subspace iteration method to calculate the perturbed modal parameters. Finally, the forward-difference algorithm is used to calculate the modal sensitivity. Two numerical examples are used to verify the superiority of the proposed modal sensitivity algorithm. Taking the 1952-bar structure as an example, the calculation time of the proposed algorithm is only 8.3 % and 18.2 % of that of the existing approximate modal superposition method and the improved Nelson’s method, respectively. In terms of calculation accuracy, the results obtained by the proposed method are exactly the same as the exact solution when the perturbation scale is set to 10−5. It is found that the proposed method significantly reduces computation time compared to the existing methods on the premise that the calculation accuracy is basically unchanged.

An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data.

DNA-based data storage is a new technology in computational and synthetic biology, that offers a solution for long-term, high-density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns. Therefore, this study proposes an Effective DNA Storage (EDS) approach for archiving medical MRI data. The EDS approach incorporates three key components (i) a novel fraction strategy to address the critical issue of rotating encoding, which often leads to data loss due to single base error propagation; (ii) a novel rule-based quaternary transcoding method that satisfies bio-constraints and ensure reliable mapping; and (iii) an indexing technique designed to simplify random search and access. The effectiveness of this approach is validated through computer simulations and biological experiments, confirming its practicality. The EDS approach outperforms existing methods, providing superior control over bio-constraints and reducing computational time. The results and code provided in this study open new avenues for practical DNA storage of medical MRI data, offering promising prospects for the future of medical data archiving and retrieval.

Quantum computing for several AGV scheduling models

Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

Fast Pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning

The Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSP) is a complex challenge in manufacturing, requiring balancing multiple, often conflicting objectives. Traditional methods, such as Multi-Objective Evolutionary Algorithms (MOEA), can be time-consuming and unsuitable for real-time applications. This paper introduces a novel Graph Reinforcement Learning (GRL) approach, named Preference-Conditioned GRL, which efficiently approximates the Pareto set for MOFJSP in a parallelized manner. By decomposing the MOFJSP into distinct sub-problems based on preferences and leveraging a parallel multi-objective training algorithm, our method efficiently produces high-quality Pareto sets, significantly outperforming MOEA methods in both solution quality and speed, especially for large-scale problems. Extensive experiments demonstrate the superiority of our approach, with remarkable results on large instances, showcasing its potential for real-time scheduling in dynamic manufacturing environments. Notably, for large instances (50 × 20), our approach outperforms MOEA baselines with remarkably shorter computation time (less than 1% of that of MOEA baselines). The robust generalization performance across various instances also highlights the practical value of our method for decision-makers seeking optimized production resource utilization.

A neural network inverse problem solution for a MIMO magnetic levitation actuator

A multiple-input and multiple-output planar actuator is proposed, which utilizes mechanically driven stator magnet arrays to levitate a permanent magnet mover. A state of levitation and actuation is obtained by mechanically altering the orientation of the stator magnets to control the forces and torques on the mover. A challenge for the design and control of the actuator is inverting the relationship between the force and stator magnet rotation angles, as there is no closed-form analytical solution. In this study, a feed-forward neural network is applied to model the forward relation between stator magnet angle input and a force and torque output to reduce the forward computation time for the design process and for error estimation in real-time applications. Additionally, the neural network is considered for inverting the solution for a motion profile sampled at 1000 Hz. The developed forward model is able to calculate the forces and torques on the mover a factor 10 faster than the equivalent charge or Fourier model with an absolute error of 3 mN and 0.1 mNm for the forces and torques, respectively, and a feed-forward neural network is able to accurately learn an inverse solution for small motion profiles.

Sample-Adaptive Classification Inference Network

Existing pre-trained models have yielded promising results in terms of computational time reduction. However, these models only focus on pruning simple sentences or less salient words, while neglecting the treatment of relatively complex sentences. It is frequently these sentences that cause the loss of model accuracy. This shows that the adaptation of the existing models is one-sided. To address this issue, in this paper, we propose a sample-adaptive training and inference model. Specifically, complex samples are extracted from the training datasets and a dedicated data augmentation module is trained to extract global and local semantic information of complex samples. During inference, simple samples can exit the model via the Sample Adaptive Exit Mechanism, Normal samples pass through the whole backbone model before inference, while complex samples are processed by the Characteristic Enhancement Module after passing through the backbone model. In this way, all samples are processed adaptively. Our extensive experiments on classification tasks datasets in the field of Natural Language Processing demonstrate that our method enhances model accuracy and reduces model inference time for multiple datasets. Moreover, our method is transferable and can be applied to multiple pre-trained models.

A Framework for Simultaneous Task Allocation and Planning under Uncertainty

We present novel techniques for simultaneous task allocation and planning in multi-robot systems operating under uncertainty. By performing task allocation and planning simultaneously, allocations are informed by individual robot behaviour, creating more efficient team behaviour. We go beyond existing work by planning for task reallocation across the team given a model of partial task satisfaction under potential robot failures and uncertain action outcomes. We model the problem using Markov decision processes, with tasks encoded in co-safe linear temporal logic, and optimise for the expected number of tasks completed by the team. To avoid the inherent complexity of joint models, we propose an alternative model that simultaneously considers task allocation and planning, but in a sequential fashion. We then build a joint policy from the sequential policy obtained from our model, thus allowing for concurrent policy execution. Furthermore, to enable adaptation in the case of robot failures, we consider replanning from failure states and propose an approach to preemptively replan in an anytime fashion, replanning for more probable failure states first. Our method also allows us to quantify the performance of the team by providing an analysis of properties such as the expected number of completed tasks under concurrent policy execution. We implement and extensively evaluate our approach on a range of scenarios. We compare its performance to a state-of-the-art baseline in decoupled task allocation and planning: sequential single-item auctions. Our approach outperforms the baseline in terms of computation time and the number of times replanning is required on robot failure.