To address the problems of traditional vehicle detection models regarding equipment requirements, detection accuracy, and overlapping target leakage rate, this study proposes an improved vehicle target detection algor...
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Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken...
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Tomato plants are vulnerable to different types of diseases, which can lead to significant reductions in yield and quality. Accurate and early detection is therefore crucial to mitigate these losses. This research pro...
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The prediction of legal judgments is based on the description of case facts to predict the final charges. Through judgment prediction technology, the judicial system can handle a large number of cases more efficiently...
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In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essenti...
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In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.
Electric vehicles(EVs)have gained prominence in the present energy transition *** adoption of EVs necessitates an accurate State of Charge estimation(SoC)*** predictive SoC estimations with smart charging strategies n...
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Electric vehicles(EVs)have gained prominence in the present energy transition *** adoption of EVs necessitates an accurate State of Charge estimation(SoC)*** predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions,marking a crucial milestone in sustainable electric vehicle *** this research study,machine learning methods,particularly Artificial Neural Networks(ANN),are employed for SoC estimation of LiFePO4 batteries,resulting in efficient and accurate estimation *** investigation first focuses on developing a custom-designed battery pack with 12V,4 Ah capacity with a facility for real-time data collection through a dedicated hardware *** voltage,current and open-circuit voltage of the battery are monitored with computerized battery *** battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry *** components are derived for the collected battery data set and analyzed for feature *** principal components were generated as input parameters for the developed *** Stopping for the ANN was also implemented to achieve faster convergence of the *** considering eleven combinations for ten different optimizers loss function is *** analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of *** proposed algorithm was also implemented for two different types of datasets,a UDDS drive cycle and a standard cell-level *** results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.
In a binary granular system composed of two types of particles with different granule sizes and the same density,particle sorting occurs easily during the flow *** segregation pattern structure is mainly affected by t...
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In a binary granular system composed of two types of particles with different granule sizes and the same density,particle sorting occurs easily during the flow *** segregation pattern structure is mainly affected by the granular velocity and granular concentration in the flow *** paper reports on the experimental velocity and concentration measurement results for spherical particles in a quasi-two-dimensional rotating *** relationship between the granular velocity along the depth direction of the flow layer and granular concentration was established to characterize structures with different degrees of *** corresponding relationships between the granular velocity and concentration and the segregation pattern were further analyzed to improve the theoretical models of segregation(convection-diffusion model and continuous flow model)and provide a reference for granular segregation control in the production process.
In dense crowd scenes, the scale of human heads varies greatly, and current visual transformer models lack learning of different scales, resulting in the model being unable to perceive the target scale changes of huma...
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The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections an...
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The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.
To enable message transmission among sensors and equipment,power line communication(PLC)is a widely adopted smart ***,due to the occurrence of impulsive noise(IN),reliable transmissions over PLC channels in the smart ...
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To enable message transmission among sensors and equipment,power line communication(PLC)is a widely adopted smart ***,due to the occurrence of impulsive noise(IN),reliable transmissions over PLC channels in the smart grid are ***,in this paper,we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method,where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time *** detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme,we develop an intelligent algorithm based on the long short-term memory *** prevent the useful signals from being eliminated as IN signals,we propose the accelerated proximal gradient method(APGM)based on tone reservation to reduce the peak-to-average power ratio(PAPR)for the transmitting signals with low computational *** addition,the closed-form expression of the bit error rate(BER)is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the *** results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation *** addition,the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.
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