Along with the explosive demand for massive data computation, federated local-edge-cloud computing enables many IoT task offloading processes and has recently gained widespread attention in consumer-centric healthcare...
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Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet ***,some of the nodes in IoT are mobile and dynamic in *** maintaining the link...
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Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet ***,some of the nodes in IoT are mobile and dynamic in *** maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data *** such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of *** to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the *** existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the *** stability helps us to define whether the node is within or out of a coverage *** paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and *** this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability *** the existing routes,the sink node will choose the optimal route which is having less link stability *** stable link is determined by evaluating link stability value using distance and ***-energy of the node is estimated using the current energy and the consumed *** energy is estimated using transmitted power and the received *** bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.
To address the nonlinearities and external disturbances in unstructured and complex agricultural environments,this paper investigates an autonomous trajectory tracking control method for agricultural ground ***,this p...
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To address the nonlinearities and external disturbances in unstructured and complex agricultural environments,this paper investigates an autonomous trajectory tracking control method for agricultural ground ***,this paper presents the design and implementation of a lightweight,modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster *** vehicle comprises drive wheel modules,passive wheel modules,battery modules,a vehicle frame,a sensor system,and a control ***,a novel robust trajectory tracking method was proposed,utilizing an improved pure pursuit ***,an Online Particle Swarm Optimization Continuously Tuned PID(OPSO-CTPID)controller was introduced to dynamically search for optimal control gains for the PID *** results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control *** validate the performance,the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural *** outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in *** proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error(RMSE)of the curve and straight lines,given a suitable set of parameters,meeting the requirements of agricultural *** findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots.
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy conce...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Ne...
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The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.
The Computational Visual Media(CVM)conference series is intended to provide a prominent international forum for exchanging innovative research ideas and significant computational methodologies that either underpin or ...
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The Computational Visual Media(CVM)conference series is intended to provide a prominent international forum for exchanging innovative research ideas and significant computational methodologies that either underpin or apply visual media.
Particle-fluid system is one of the most popular systems in chemical *** to complex interface structure and high-velocity turbulence,the momentum and mass transfer exhibit nonlinear characteristics,which pose a great ...
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Particle-fluid system is one of the most popular systems in chemical *** to complex interface structure and high-velocity turbulence,the momentum and mass transfer exhibit nonlinear characteristics,which pose a great challenge for further study and *** solve this problem,computational mass transfer(CMT)emerged and has been proved to be effective in deeply exploring the mass transfer behavior of particle-fluid ***,this paper reviews recent gas-solid numerical studies of turbulence issues from empirical to theoretical,then discusses interphase mass transfer rate models and the interfacial interaction ***,the present study particularly reviews researches on mass transfer process of fixed and fluidized regime by CMT,providing reliable analysis of turbulent anisotropy diffusivity as well as multiscale structure and presenting theoretical instruction for the industrial optimization of mass transfer processes in chemical engineering.
A coverage control strategy based on an improved generalized normal distribution optimization algorithm is proposed for coverage optimization of sensor networks. Firstly, IGNDO uses a combination of Logistic and Tent ...
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In this paper we provided an insightful exploration into the critical role of feature matching in enhancing the efficacy of e-commerce recommendation systems. By meticulously analyzing user data and product characteri...
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The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla *** today’s technology-driven era,where precise tools f...
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The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla *** today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla *** existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document ***,no prior research has specifically targeted the unique needs of Bangla handwritten city name *** bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name *** emphasis on practical data for system training enhances *** research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal *** study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN *** encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and *** recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.
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