Computational Efficiency 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.

A comparative analysis of convolutional neural networks for breast cancer prediction

Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.

A novel identifiable data sharing mechanism for multiple participants in cloud computing

Recent applications and growth on the internet have generated a lot of popularity and adoption of cloud computing which aims to assure the various computing resources. Data storage is one of the primary resources offered by the cloud; however, considering the multiple users in the particular cloud raises major concerns due to security. Recent researches shown great potential for providing efficient data sharing with multiple users. However, tracing of the data provider is still concerned to be a major issue. Hence, this research work proposes identifiable data sharing for multiple users (IDSMU) mechanism which aims to provide security for multiple users in a particular cloud group. At first, IDSMU creates the general participants (GP)-key for secure access to data. Further, IDSMU creates the trusted participants (TP) based on the reputation which further helps in creating the key generation. A novel signature scheme is used for identifying the participants; IDSMU is evaluated on computation count and efficiency is proved by comparing with an existing model considering computation count.

Verifiable data distribution technique for multiple applicants in a cloud computing ecosystem

<span lang="EN-US"><span lang="EN-US">Cloud computing is the most exploited research technology in both industry and academia due to wide application and increases in adoption from global organizations. In cloud, computing data storage is one of the primary resources offered through cloud computing, however, an increase in participants raises major security concerns, as the user has no hold over the data. Furthermore, recent research has shown great potential for efficient data sharing with multiple participants. Existing researches suggest complicated and inefficient cloud security architecture. Hence, this research work proposes identifiable data sharing for multiple users (IDSMU) mechanism, which aims to provide security for multiple users in a particular cloud group. A novel signature scheme is used for identifying the participants, further verification of the Novel Signature Scheme is proposed along with a retraction process where the secret keys of the participant and the sender is cross-verified; at last, a module is designed for the elimination of any malicious participants within the group. IDSMU is evaluated on computation count and efficiency is proved by comparing with an existing model considering computation count. IDSMU performs marginal improvisation over the existing model in comparison with the existing model using the novel signature scheme. </span><br /></span>

Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.

Structural optimization of serpentine channel water-cooled plate for lithium-ion battery modules based on multi-objective Bayesian optimization algorithm

Maintaining the battery within its optimal operating temperature range and preventing thermal runaway is crucial. Serpentine channel water-cooled plate (SCWCP) has been widely employed in battery pack cooling. The challenge lies in enhancing the cooling efficiency of SCWCP while minimizing energy consumption. Due to the high efficiency and robustness of the multi-objective Bayesian optimization (MOBO), it is employed to systematically optimize the SCWCP for lithium batteries. The width, depth, and turning radius of the serpentine flow channels are optimized to minimize both the maximum battery module temperature (Tmax) and the pumping power (PP) of the SCWCP. The MOBO process integrates structural parameter adjustments into Computational Fluid Dynamics (CFD) simulations through an automated iterative approach. Subsequently, a Pareto front is generated based on Tmax and PP, and the K-means clustering algorithm identifies four design solutions with different performance orientations. Compared with the initial design, Tmax of the optimal design decreases slightly, but with a reduction in PP of 71 %. Compared to other evolutionary algorithms, MOBO algorithm exhibits superior computational efficiency in providing an optimal design solution set at a lower computational cost.

Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network

Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.

Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model

Dental periapical lesions, commonly associated with inflammation around the tooth apex, pose a significant challenge in early diagnosis and treatment. This study introduces a novel approach for the detection of dental periapical lesions through the integration of Retinex-based image enhancement techniques and a lightweight deep learning model. The Retinex algorithm is employed to enhance the radiographic images, addressing issues related to inconsistent illumination and contrast. Subsequently, a tailored lightweight deep learning model is designed to efficiently extract relevant features from the enhanced images. Present methodology leverages a dataset of dental radiographs to train and evaluate the deep learning model, incorporating a diverse range of periapical lesion cases. The model is optimized for computational efficiency while maintaining high accuracy, making it suitable for deployment in resource-constrained environment. To enhance precision in lesion detection, the U-Net segmentation technique has been incorporated, providing a sophisticated approach to delineate and analyze specific areas of interest within the radiographic images. This addition further refines our diagnostic framework, contributing to the robustness of lesion identification. Experimental results demonstrate the effectiveness of the Retinex-based image enhancement in improving the visibility of periapical lesions. The lightweight deep learning model exhibits promising performance in accurately detecting and classifying dental periapical lesions, showcasing its potential for early and efficient diagnosis. The results obtained from the present model are compared with those from Convolutional Neural Network (CNN) as well as with diagnosis of expert practitioners and the model is observed to perform very well. The study contributes to the advancement of computer-aided diagnostic tools in dentistry, offering a scalable and accessible solution for the identification of dental periapical lesions through the fusion of image enhancement and lightweight deep learning techniques.

Comparative Analysis of VGG16, RESNET50, AND CNN Models for Lung Disease Prediction: A Deep Learning Approach

Abstract: Utilizing deep learning methods in medical image analysis has shown promise in enhancing disease detection and diagnosis. In this study, we conducted a detailed comparative analysis of threewidely recognized convolutional neural network (CNN) architectures: VGG16, ResNet50, and a custom CNN model tailored for pneumonia and COVID-19 detection from chest X-ray images. Leveraging transfer learning techniques and meticulously curated datasets, we evaluated the models' performance in accurately identifying respiratory diseases. Our investigation utilized two publicly available datasets, the ChestX-ray14 dataset and the NIH Chest X-ray Dataset, both annotated for pneumonia and COVID-19. Prior to model training, we conducted thorough preprocessing to ensure optimal data quality and consistency. Through rigorous experimentation, we assessed the models' accuracy, sensitivity, and specificity in disease detection. The results revealed nuanced differences in model performance across disease categories. While VGG16 demonstrated robust accuracy in pneumonia detection, ResNet50 exhibited enhanced sensitivity and specificity in identifying COVID-19 cases. Our custom CNN model, leveraging insights from both architectures, showcased competitive performance, emphasizing the importance of tailored model design for optimal diagnostic outcomes. Through comprehensive analysis and discussion, we elucidated the strengths and limitations of each model, considering factors such as computational efficiency, interpretability, and generalizability. Our findings underscore the potential of deep learningbased diagnostic tools in supporting healthcare professionals in timely and accurate disease diagnosis

Hand Written Digit Recognition System

Abstract: This research paper provides a comprehensive analysis of the application of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for the task of handwritten digit recognition, focusing on the extensively studied MNIST dataset. The study delves into the strengths and weaknesses of both approaches, considering various aspects such as accuracy, computational efficiency, and robustness. Through an in-depth exploration of the literature and empirical evidence, this paper aims to offer valuable insights into the advancements, challenges, and future directions in the field of handwritten digit recognition.

Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle

The classical Pauli particle (CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field (AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments, demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.

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.

Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds.

Decoding gestures from the upper limb using noninvasive surface electromyogram (sEMG) signals is of keen interest for the rehabilitation of amputees, artificial supernumerary limb augmentation, gestural control of computers, and virtual/augmented realities. We show that sEMG signals recorded across an array of sensor electrodes in multiple spatial locations around the forearm evince a rich geometric pattern of global motor unit (MU) activity that can be leveraged to distinguish different hand gestures. We demonstrate a simple technique to analyze spatial patterns of muscle MU activity within a temporal window and show that distinct gestures can be classified in both supervised and unsupervised manners. Specifically, we construct symmetric positive definite (SPD) covariance matrices to represent the spatial distribution of MU activity in a time window of interest, calculated as pairwise covariance of electrical signals measured across different electrodes. This allows us to understand and manipulate multivariate sEMG timeseries on a more natural subspace - the Riemannian manifold. Furthermore, it directly addresses signal variability across individuals and sessions, which remains a major challenge in the field. sEMG signals measured at a single electrode lack contextual information such as how various anatomical and physiological factors influence the signals and how their combined effect alters the evident interaction among neighboring muscles. As we show here, analyzing spatial patterns using covariance matrices on Riemannian manifolds allows us to robustly model complex interactions across spatially distributed MUs and provides a flexible and transparent framework to quantify differences in sEMG signals across individuals. The proposed method is novel in the study of sEMG signals and its performance exceeds the current benchmarks while maintaining exceptional computational efficiency.

Smoothed particle hydrodynamics based numerical study of hydroplaning considering permeability characteristics of runway surface

AbstractThe presence of water films on a runway surface presents a risk to the landing of aircraft. The tire of the aircraft is separated from the runway due to a hydrodynamic force exerted through the water film, a phenomenon called hydroplaning. Although a lot of numerical investigations into hydroplaning have been conducted, only a few have considered the impact of the runway permeability. Hence, computational problems, such as excessive distortion and computing efficiency decay, may arise with such numerical models when dealing with the thin water film. This paper presents a numerical model comprising of the tire, water film, and the interaction with the runway, applying a mathematical model using the smoothed particle hydrodynamics and finite element (SPH-FE) algorithm. The material properties and geometric features of the tire model were included in the model framework and water film thicknesses from 0.75 mm to 7.5 mm were used in the numerical simulation. Furthermore, this work investigated the impacts of both surface texture and the runway permeability. The interaction between tire rubber and the rough runway was analyzed in terms of frictional force between the two bodies. The SPH-FE model was validated with an empirical equation proposed by the National Aeronautics and Space Administration (NASA). Then the computational efficiency of the model was compared with the traditional coupled Eulerian-Lagrangian (CEL) algorithm. Based on the SPH-FE model, four types of the runway (Flat, SMA-13, AC-13, and OGFC-13) were discussed. The simulation of the asphalt runway shows that the SMA-13, AC-13, and OGFC-13 do not present a hydroplaning risk when the runway permeability coefficient exceeds 6%.

A Hybrid Finite Element Method – Kirchhoff Approximation Method for Modeling Acoustic Scattering from an Underwater Vehicle Model with Alberich Coatings with Periodic Internal Cavities

Anechoic tiles can significantly reduce the echo intensity of underwater vehicles, thereby increasing the difficulty of detecting such vehicles. However, the computational efficiency of conventional methods such as the finite element method (FEM) and the boundary element method (BEM) has its limitations. A fast hybrid method for modeling acoustic scattering from underwater vehicles with anechoic tiles with periodic internal cavities, is developed by combining the Kirchhoff approximation (KA) and FEM. The accuracy and rapidity of the KA method were validated by FEM. According to the actual situation, the reflection coefficients of rubber materials with two different structures under rigid backing are simulated by FEM. Using the KA method, the acoustic scattering characteristics of the underwater vehicle with anechoic tiles are obtained by inputting the reflection coefficients and the target’s geometric grid. Experiments on the monostatic target strength (TS) in the frequency range of 1 to 20 kHz and time domain echo characteristics of acoustic scattering on a benchmark scale model with anechoic tiles are conducted. The research results indicate that the TS values and echo characteristic curves of the KA solutions closely approximate the experimental results, which verifies the accuracy of the KA method in calculating the TS and echo characteristics of underwater vehicles with anechoic tiles.

Using Brownian model to study the effect of temperature on diffusion coefficient

This study employs molecular simulation techniques, particularly the Brownian motion model, to investigate the influence of temperature on diffusion coefficients. Leveraging Einsteins relationship for calculating diffusion coefficients and a one-dimensional random walk model, we systematically explore the impact of simulation direction, particle quantity, and simulation duration on diffusion behavior. The research extends its scope to three-dimensional Brownian motion simulations, offering insights into the stochastic motion of particles in a fluid. The primary objective is to analyze the relationship between temperature and diffusion coefficients. Through rigorous linear regression analysis, a significant and strong linear association is identified, demonstrating that an increase in temperature correlates with an increase in the diffusion coefficient. The research not only contributes to the fundamental understanding of molecular motion but also provides practical recommendations for simulation parameters, especially in resource-constrained computational environments. The study acknowledges certain limitations in the current Brownian motion algorithm and proposes avenues for future research to enhance computational efficiency and precision. This abstract encapsulates the key methodologies, findings, and implications of the research, laying the foundation for a comprehensive exploration of temperature-dependent diffusion coefficients in molecular systems.

Modeling and simulation of three-phase IGBT full-bridge inverter circuit based on FPGA

The field of motor drive makes extensive use of electronic power modeling and simulation of three-phase IGBT full-bridge inverter circuits. The accuracy and computational efficiency of these models have a direct impact on the dependability of the motor control system. The majority of earlier research focused solely on static processes of turning on and off in IGBTs, disregarding the transient proesses that occur when three-phase IGBT full-bridge inverter circuits are switched at high frequencies. This has an impact on the circuits' accuracy in real-time simulation. Therefore, this paper proposes and builds a field-programmable logic gate array (FPGA)-based steady-state and transient dual-phase three-phase IGBT full-bridge inverter circuit model for the static and transient characteristics of the insulated gate bipolar transistor (IGBT) element in the circuit. Depending on whether or not the switching states of the six IGBTs in the three-phase IGBT full-bridge inverter circuit are altered, the simulation process is split into steady state and transient phases. In the steady state phase with large step size, the circuit is discretized using the binary L/C approach. In the transient phase, the transient process is divided into several small-step-long time domains. Real-time simulation waveforms are generated by interleaving and combining the multistage fitting method's solution of the circuit's transient waveforms at tiny step lengths with the steady state phase. Finally, in order to demonstrate the accuracy of the circuit model in this work, the simulation results of the two-stage three-phase IGBT full-bridge inverter circuit model based on FPGA are compared with those of the conventional ideal model for waveform comparison and data analysis.

Bayesian dynamic modelling for probabilistic prediction of pavement condition

Significant funds have been allocated to maintain road networks each year in developed countries. Performance prediction is crucial for pavement management systems to adjust working plans and budget allocation. As a Bayesian nonparametric method, Gaussian process regression (GPR) is powerful in predicting nonlinear time series and quantifying uncertainty. However, it remains computationally intensive and fails to adapt to the time-varying characteristics. To address such issues, a dynamic GPR model is proposed for probabilistic prediction of the International Roughness Index (IRI) for flexible pavements. A moving window strategy is developed to substantially shrink the size of training data, which effectively alleviates computational cost and thus leads to a dynamic GPR. A genetic algorithm is then adopted to determine the optimal window size by considering the trade-off between computational efficiency and accuracy. A dataset acquired from Long-Term Pavement Performance (LTPP) is used to demonstrate the feasibility of the dynamic GPR. Its performance is compared to traditional GPR as well as dynamic and static Bayesian linear regression (BLR) models. The comparison results indicate that the proposed dynamic GPR can increase the accuracy by 0.86, 1.52, and 2.27 times for dynamic BLR, static GPR, and static BLR, respectively. It exhibits the best results in terms of accuracy and uncertainty metrics due to its nonlinear modelling and time-varying ability.

Predicting the responses of European grassland communities to climate and land cover change.

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.