Network Model Research Articles

Experimental study and neural network model based prediction of layer thickness influence on LPBF IN625 single track geometry

The performance of interacting metal surfaces having sliding motion is enhanced by fabricating microfeatures on-top of the surfaces. Laser powder bed fusion (LPBF) is used to fabricate the microfeatures of IN625 on-top of a flat surface. In LPBF, geometrical characteristics of microfeatures are based on the energy supplied and the volume of material melted (layer thickness) at that instant of time. There is very few research reported in open literature on the effect of layer thickness on microfeatures geometry during LPBF. In this work, a series of single tracks is fabricated with different combinations of laser power and scanning speed for 75 μm, 100 μm, and 125 μm layer thickness and its effect on single track geometrical characteristics and microstructure was evaluated. It has been found that laser power and scanning speed have a significant influence on the track geometry irrespective of layer thickness. The minimum linear energy density of 2.5 J/mm is considered to fabricate the continuous track formation for investigated layer thickness. From statistical analysis, it is observed that the laser power and scanning speed shows higher contribution on track width and remelting depth, respectively. Whereas, layer thickness shows a higher influence on track height and contact angle of single track as compared to laser power and scanning speed. In 125 μm layer thickness, the volume of material melting is increased which encourages the gravity effect to deform the melt pool and produces larger track width compare to 100 μm layer thickness. Further, the cell spacing is gradually increased with layer thickness resulting increase in the solidification period. The shape controllability of the single track is significantly increased with layer thickness, but as keeps on increasing layer thickness due to the larger volume of molten metal, the melt pool losses ability to maintain convex shapes. An Artificial Neural Network (ANN) model is developed to understand the relationship between the LPBF process parameters and single track geometrical characteristics to predict the responses for a wide range of process parameters. The multiple response neural network model shows excellent agreement with experimental results with R2 value greater than 0.95.

Bayesian Analysis of Exponential Random Graph Models Using Stochastic Gradient Markov Chain Monte Carlo

The exponential random graph model (ERGM) is a popular model for social networks, which is known to have an intractable likelihood function. Sampling from the posterior for such a model is a long-standing problem in statistical research. We analyze the performance of the stochastic gradient Langevin dynamics (SGLD) algorithm (also known as noisy Longevin Monte Carlo) in tackling this problem, where the stochastic gradient is calculated via running a short Markov chain (the so-called inner Markov chain in this paper) at each iteration. We show that if the model size grows with the network size slowly enough, then SGLD converges to the true posterior in 2-Wasserstein distance as the network size and iteration number become large regardless of the length of the inner Markov chain performed at each iteration. Our study provides a scalable algorithm for analyzing large-scale social networks with possibly high-dimensional ERGMs.

A Bionic Localization Memristive Circuit Based on Spatial Cognitive Mechanisms of Hippocampus and Entorhinal Cortex.

In this article, a bionic localization memristive circuit is proposed, which mainly consists of head direction cell module, grid cell module, place cell module and decoding module. This work modifies the two-dimensional Continuous Attractor Network (CAN) model of grid cells into two one-dimensional models in X and Y directions. The head direction cell module utilizes memristors to integrate angular velocity and represents the real orientation of an agent. The grid cell module uses memristors to sense linear velocity and orientation signals, which are both self-motion cues, and encodes the position in space by firing in a periodic mode. The place cell module receives the grid cell module's output and fires in a specific position. The decoding module decodes the angle or place information and transfers the neuron state to a 'one-hot' code. This proposed circuit completes the localizing task in space and realizes in-memory computing due to the use of memristors, which can shorten the execution time. The functions mentioned above are implemented in LTSPICE. The simulation results show that the proposed circuit can realize path integration and localization. Moreover, it is shown that the proposed circuit has good robustness and low area overhead. This work provides a possible application idea in a prospective robot platform to help the robot localize and build maps.

Prediction of HVAF thermal spraying parameters and coating properties based on improved WOA-ANN method

In this study, high-velocity air fuel (HVAF) thermal spray technology was utilized to deposit Hastelloy C276 powder onto 316L stainless steel substrates. The effects of various spray process parameters such as propane pressure, air pressure, spray distance, and powder feeding rate on the microstructure, microhardness, and corrosion resistance of the coatings were investigated. In addition, the coupling effects among multiple spray parameters were considered by employing a combined approach with the Improved Whale Optimization Algorithm (IWOA) and Artificial Neural Network (ANN) model, and the mapping relationships between the spray process parameters and coating porosity, microhardness, and corrosion current density was established. The results revealed that the prepared C276 coating exhibits lower porosity, higher microhardness, and exceptional corrosion resistance. And the IWOA-ANN model outperforms the ANN model and WOA-ANN model in the prediction accuracy and stability for all three performance measures, particularly for porosity and microhardness. Furthermore, the comparison between experimental results and predicted results validates the reliability and accuracy of the trained IWOA-ANN model, demonstrating its efficacy in predicting HVAF spray parameters and coating properties.

Investigation of agricultural waste and ZrB2 nanoparticles with reinforcement in aluminum alloy matrix

Agricultural waste has the potential to serve several purposes, such as reinforcement. In recent times, researchers have incorporated agricultural waste into various materials with the aim of enhancing their qualities. By acting as a kind of reinforcement, it has the potential to decrease the quantity of agricultural waste that needs further processing. This work enhances the area of advanced materials by examining the characteristics of Al 7175 alloy reinforced with zirconium diboride (ZrB2) nanoparticles and rice husk ash, which is a byproduct of agriculture. Understanding and improving the wear resistance of composites is crucial in several industrial applications. This study involves the incorporation of ZrB2 nanoparticles into the Al 7175 alloy at three distinct concentrations, with the addition of 2.5 % rice husk. This study examines the correlation between wear parameters and responses (Cf), including friction force (FF), wear rate (Sf), and friction coefficient. Sliding distance, load, composition, and speed are among the wear parameters. The aforementioned combinations result in a load of 25.2 N, a rotational speed of 400 rpm, a sliding distance of 71.2 m, and a composition of 5 %. Taguchi signal-to-noise ratio (SNR) analysis is used to identify optimal combinations for minimizing Cf, Sf, and FF. Confirmation tests are conducted in order to ensure that the results align with the expected optimal combinations. Data scientists may also use an Artificial Neural Network (ANN) model as a valuable tool. It utilizes input data to forecast replies. The ANN model demonstrates strong performance throughout testing, validation, and training, with an overall prediction rate of 87.873 %. The study's results indicate that reducing the load and speed, while increasing the concentration of ZrB2 nanoparticles, may effectively decrease the wear rate. In addition, the use of rice husk enhances the wear resistance of the base alloy by 28 %. The alloy Al 7175 may experience a reduction in wear rate and friction coefficients by the incorporation of 2.5 % rice husk and 5 % ZrB2 nanoparticles.

From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies

The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing the vulnerability of spring wheat to drought timing and severity in the Canadian Prairies. To achieve this, a bayesian neural network (BNN) model was developed that predicts high-resolution wheat yields and uncertainties using the CDM and normalized difference vegetation index (NDVI) data from 2010 to 2021. Model performance is comparable to previous studies, with errors ranging from 13% to 18% of mean yields and R2 values from 0.43 to 0.70 that peak in early August. The model was then applied under various drought conditions to evaluate how different drought timings and severities affect yield vulnerability. The results demonstrate that drought exposure from June to August reduces wheat yields in proportion to drought severity while abnormally dry conditions may have a positive impact of yield, particularly in August. Yield uncertainty decreases substantially from June to July while yield prediction accuracy increases, suggesting that mitigation efforts are most effective prior to July. The final component of the research involved creating operational risk maps that integrate the yield vulnerability assessment with the CDM. These maps were applied in a case study to demonstrate their utility in providing actionable, real-time information for agricultural stakeholders. Overall, this study contributes to the body of knowledge regarding crop sensitivities to varying drought conditions and represents a methodological advancement by enabling high-resolution, operational agricultural risk assessments at scale. By integrating detailed vulnerability data with drought hazard monitoring from the CDM, the information provided by these risk maps can be leveraged by agricultural stakeholders in near real-time to make well-informed decisions that mitigate the impacts of drought on wheat production, enhancing the resilience of agricultural systems to climatic variability.

Wind Energy Harvesting with Building-Integrated Ducted Openings: CFD Simulation and Neural Network Optimization

Building-integrated wind energy harvesting offers a promising local and decentralized energy generation solution, reducing transmission losses and infrastructure expenses. However, the complex urban wind patterns demand accurate aerodynamic optimization for optimal performance. This study seeks to optimize the geometric design of building-integrated ducted openings to maximize wind energy harvesting. The research aims to create a data-driven model predicting the openings' performance accurately, explore the inter-linked impacts of different geometrical variables, find optimal configurations across different building heights, and understand underlying aerodynamic mechanisms. The proposed design comprises a nozzle, throat, and diffuser. To increase power density, a data-driven design optimization method is employed. The optimization process involves determining the optimum opening height from the ground, along with five geometrical variables: inlet diameter, throat diameter, outlet diameter, nozzle length, and diffuser length. A comprehensive test matrix is developed using design of experiments (DoE) to build an artificial neural network (ANN) model. This neural network is developed using computational fluid dynamics (CFD) simulations validated with experimental data. Subsequently, a gradient-based global optimization method fine-tunes the neural network and identifies optimal designs for various heights. The research concludes that nozzle and diffuser opening angles within the range of 19° ≤ ΦNoz ≤ 23°and 7.5° ≤ ΦDif ≤ 8.2°, respectively, result in the optimal converging-diverging ducted opening geometry. This optimal opening can yield a power density increase of 31.5 to 35.5 times when compared to freestream flow and 17.5 to 22.4 times in comparison to cylindrical openings, depending on the opening location along the building height.

Design of a novel deep network model for spinal cord injury prediction

Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.

A machine-learning based framework for design and characterization of honeycombs with partial self-similar hierarchical architectures

Honeycomb structures with hierarchical topology are widely used in engineering fields for their excellent mechanical properties. Multiphase topology is proved to possess a larger design space than the traditional hierarchical topology. In this study, a partial self-similar hierarchical honeycomb (PSSHH), comprising of zero-order cell and first-order cell, was designed. Numerical simulations, validating against the experimental results, were carried out to investigate the in-plane crushing performance of PSSHHs with different configurations. An artificial neural network model was trained using the results of simulations. The ability and effectiveness of artificial neural networks in predicting and characterizing the crushing responses of PSSHH were evaluated through comparison with simulations. The model was primarily employed for optimizing the design of PSSHH to enhance the crushing performance. The results indicate that the optimized PSSHH outperforms traditional hexagonal honeycombs and fully self-similar hierarchical honeycombs in terms of initial peak force, mean crushing force, and energy absorption. Furthermore, the effect of number and organized patterns of the first-order cells along the loading direction on the mechanical properties of PSSHH is examined. The findings reveal that the formation and development of localized collapse bands are significantly affected by the number and distribution of hierarchical first-order cells. The deformation, which involves a sequential crushing process with the initial collapse of specific zero-order cells followed by the subsequent collapse of first-order cells, contributes to enhancing the crashworthiness performance of PSSHH.

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chronic Kidney Disease Epidemiology creatinine (CKD-EPIcr) equation without race and the estimated glomerular filtration rate (eGFR) equation developed for the Chinese population - displayed suboptimal performance in patients with neurogenic lower urinary tract dysfunction (NLUTD), which limited their clinical application for detecting changes in GFR levels in all cohorts. To develop a neural network model based on multilayer perceptron (MLP) for evaluating GFR in Chinese NLUTD patients, and compare the diagnostic performance with Cr-based multiple linear regression equations for Chinese and the CKD-EPIcr equation without race. Single-center, cross-sectional study of GFR estimation from serum Cr, demographic data, and clinical characteristics in Chinese patients with NLUTD. A total of 204 NLUTD patients, from 27 different geographic regions of China, were selected. A random sample of 141 of these subjects was included in the training sample set, and the remaining 63 patients were included in the testing sample set. The reference GFR (rGFR) was assessed by the technetium-99m-labeled diethylenetriaminepentaacetic acid (99mTc-DTPA) double plasma sample method. A neural network model based on MLP was developed to evaluate GFR in the training sample set, which was then validated in the testing sample set and compared with Cr-based GFR equations. The MLP-based model showed significant performance improvement in evaluating the difference, absolute difference, precision, and accuracy of GFR estimation compared with the Cr-based GFR equations. Additionally, compared with the rGFR, we found that the MLP-based model provided an acceptable level of accuracy (greater than 85%, which was within a 30% deviation from the rGFR). The MLP-based model offered significant advantages in estimating GFR in Chinese NLUTD patients, and its application could be suggested in clinical practice.

Dynamic base station allocation for 6G wireless networks through narrow neural network

The 6G wireless communication system will utilize the terahertz (THz) frequency band (0.1-10 THz) to meet customer demand for increased data rates and ultra-high-speed communication in future applications. The exponential surge in data traffic, which is supported by dynamic resource allocation. To mitigate this challenge, the use of artificial intelligence-based methods, such as narrow neural network (NNN), can help to smooth the performance of the network. In this paper, an NNN-based approach for dynamic base station allocation for 6G wireless networks is proposed 14 different 6G parameters used to train the NNN model, initially achieving an accuracy of 89.5% and an F1 score of 0.72 for 200 users. Results demonstrate the efficacy of the proposed NNN approach for dynamic decision-making in 6G networks and its potential for application in other domains where similar problems exist. Moreover, the proposed narrow neural network model shows improved results with an increase in number of users and decrease in fully connected layers and regularization strength (lambda). The validation accuracy received is 98.9% and 99.6% for thousand users with single fully connected layer, none (linear) activation function and regularization strength lambda values of 0.01 and 0.001.

Research on intelligent classification of weld defects based on improved ResNeXt network

Welding technology is widely used in the connection of metal workpieces, and then due to improper operation and other reasons, small defects may occur in the welding, which will lead to welding failure and hazardous accidents. Therefore, it is very important to accurately identify welding defects. The automatic defect identification method based on deep learning learns features directly from the full-focus mapping of weld defects and automatically classifies them; the ResNeXt network is selected for improvement, and the SK convolution unit is introduced into the original ResNeXt-50 network model to enhance the adaptive adjustment of sensory field; in order to alleviate the problem of the point-by-point convolution in the ResNeXt-50 which consumes a large amount of In order to alleviate the problem that point-by-point convolution in ResNeXt-50 consumes a large amount of computation, 1×1 group convolution is used instead of the original point-by-point convolution, and channel rearrangement is introduced to alleviate the problem of weakened information exchange of groups caused by group convolution. After improvement, the ResNeXt-50 network model can achieve a classification accuracy of 98.2%, which is 5.5% more than that of the original ResNeXt-50 on this paper's dataset, and the classification accuracy after T-sne visualization also has obvious separation boundary and clustering effects.

Game Theory-Based Optimization for Efficient IoT Task Offloading in 6G Network Base Stations

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimisation is to maximise the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimisation.

A developed convolutional neural network model for accurately and stably predicting effective thermal conductivity of gradient porous ceramic materials

Accurate and stable prediction of the effective thermal conductivity (ETC) of porous ceramic materials is of great significance for their application in areas such as optimizing the design of thermal barrier coatings and improving energy conversion efficiency. Porous ceramic materials exhibit complex porous structures with gradient porosity distributions that pose a great challenge for effective medium theory (EMT) and conventional convolutional neural networks (CNN) in attempting to accurately and stably predict the ETC of porous media. In this study, a CNN model that integrates self-attention and a multiscale feature-fusion mechanism is proposed to predict the ETC of porous media with greater accuracy and stability. The integration of the self-attention and multiscale feature-fusion mechanisms enhances the CNN's ability to learn long-range dependencies and preserve detailed information. The optimization of the CNN's accuracy and stability is visually illustrated using gradient-weighted class activation mapping (Grad-CAM) for ETC. Additionally, by employing the proposed gradient quartet structure generation set (QSGS), a gradient porous ceramic media dataset comprising 10 000 images was built to train the CNN model. Finally, the prediction results and relative error distribution of the ETC were compared across different models. Our model demonstrated improvements in statistical metrics, including a 33.7 % decrease in mean error, 25.2 % decrease in median error, and 59.6 % decrease in maximum error. The decreases in these metrics and Grad-CAM for the ETC strongly demonstrates that the model proposed in this work greatly improved the accuracy and stability of predicting the ETC of ceramic materials with gradient porosity distributions.

5G Wireless Technology: Spectrum Sharing and Heterogeneous Networks

Abstract: This paper addresses the critical challenge of 5G network cell planning, emphasizing the essential role of estimating the number of cells or base stations required for a given area and user bandwidth. The proposed model explores four scenarios with varying network parameters to determine the optimal base station deployment. The increasing demand for 5G networks necessitates a focus on small cells or femtocells, offering advantages such as consistent coverage, power adjustment, energy efficiency, and higher data rates. However, deploying excessive femtocells may lead to unnecessary handovers, requiring optimization strategies. The study introduces an analytical model for heterogeneous cellular networks, integrating fourth and fifth-generation systems. The model, represented by a two dimensional Markov Chain, employs a novel decomposition approach for analyzing system performance measures. Validation is conducted through simulation and simultaneous equation systems. The proliferation of mobile devices and data traffic necessitates dense 5G network deployments, with small cells gaining traction due to their versatile coverage, power adaptability, energy efficiency, and higher data rates. However, an excessive deployment of small cells can lead to frequent handovers, prompting the need for enhanced handover strategies and coexistence with other technologies in the 5G landscape. The paper concludes by highlighting the impending need for practical implementation testing of 5G systems, emphasizing the significant role of small cell deployments. It discusses an initiative focusing on testing small cellenabled operator business models in real-world scenarios, anticipating the pivotal role of small cells in future 5G systems.

Traffic Sign Recognition using HIICNN Stack Ensemble Method

Abstract: This project introduces a novel deep convolutional neural network (CNN) model aimed at enhancing traffic sign recognition for autonomous vehicle technology and road safety. Employing a stacking ensemble approach, we combine multiple CNNs with diverse improvement techniques, including preprocessing for adverse weather conditions and shooting errors, and data augmentation to handle class imbalance. Through adjustments in learning rates during model training, we mitigate overfitting. Our ensemble model achieves an exceptional 99.4 percent test accuracy on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, surpassing prior studies. We utilize gradient-weighted Class Activation Mapping (Grad-CAM) for model explain- ability and an evidential deep learning approach to quantify classification uncertainty. Our framework underscores the efficacy of combining preprocessing, ensemble learning, and transfer learning for superior perfor- mance and reliability in traffic sign recognition systems, contributing significantly to autonomous driving and road safety.

Crop Disease Prediction Using Web Application

Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a plant and cause farmer to lose a substantial sum yearly. Deep Learning (DL) can play a crucial role in helping farmers to prevent crop failure by early disease detection in plant leaves. In the experiment, examination conducted by Convolution Neural Network (CNN) model on dataset (which consists of images of healthy and infected leaves) to detect plant diseases and Web Application is used for real-life crop disease prediction. The proposed Web Application aims to assist farmers in detection of plant diseases by analyzing images of the plant leaves. The proposed application uses the CNN model to distinguish healthy and infected leaves. The goal is to help farmers and prevent economic loss by detecting plant diseases early

Unveiling Crash Casualties: A Neural Network Model for Crash Type Classification Using Traffic Datasets

Abstract: Traffic accidents, a significant public health burden with substantial economic costs, necessitate proactive measures for sustainable transportation. This study investigates the efficacy of neural networks in predicting crash types using traffic data. We propose a deep learning model that leverages the power of recurrent neural networks (RNNs) for sequential traffic pattern analysis and convolutional neural networks (CNNs) for extracting spatial features from traffic data. The model, trained on a labeled traffic dataset, learns to identify patterns and relationships between traffic characteristics (e.g., speed, volume, weather) and different crash types (e.g., rear-end collision, single-vehicle). The evaluation focuses on the model's accuracy in predicting crash types using metrics like precision, recall, and F1-score. This research contributes to the development of intelligent traffic management systems (ITMS) that can proactively identify and mitigate potential accidents, ultimately enhancing road safety and promoting a more sustainable transportation future.The implementation results indicate that the proposed method yields an accuracy of 82.93% approximately.

Image Colorization

Abstract: Image colorization is a complex and challenging task in computer vision, with numerous applications in art, entertainment, restoration, and more. This project aims to develop an automated image colorization system leveraging the power of deep learning techniques. The primary objective is to train a deep neural network model capable of accurately and semantically colorizing grayscale images, reproducing natural and visually appealing colour distributions. Our approach utilizes Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to learn the intricate relationships between grayscale images and their corresponding colour versions. The project involves the following key steps: Data Collection and Preprocessing, Model architecture, Training, Evaluation, Application. The project's outcome is expected to provide a powerful and versatile tool for automating image colorization tasks, offering high-quality results while preserving the artistic intent of the original images. The fusion of deep learning and computer vision techniques in this project exemplifies the potential for artificial intelligence to revolutionize image processing and creative industries.

Intelligent Document Processing for Disease Detection

Abstract: This project endeavors to develop an intelligent document processing pipeline tailored specifically for medical reports, with a primary focus on samples from Dr Lal Path lab and targeting prevalent diseases leading to kidney failures such as glomerulonephritis, chronic kidney disease, polycystic kidney disease, hypertensive nephropathy, and lupus nephritis. The proposed pipeline integrates cutting-edge technologies including the YOLOv8 object detection model for precise cropping of tabular data, Paddle OCR for accurate extraction of information from tabular images, and Fuzzy Wuzzy NLP library for filtering pertinent data from the extracted information necessary for the subsequent machine learning model. The goal is to employ a neural network model to predict potential kidney failure based on the processed medical data. This holistic approach amalgamates advanced computer vision, natural language processing, and machine learning techniques to streamline the analysis of medical reports, potentially enhancing diagnostic accuracy and clinical decision-making in nephrology.