Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early ...
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Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.
Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a *** work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS *...
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Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a *** work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS *** NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern *** order to overcome the disadvantages that NGO is prone to local optimum trap,slow convergence speed and low convergence accuracy,two strategies are introduced in the original NGO to boost the effectiveness of ***,a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population ***,a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and *** prove the effectiveness of the suggested DENGO,it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions,and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and ***,the proposed DENGO is used for FS,and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods,and therefore,DENGO is considered to be one of the most prospective FS ***'s code can be obtained at https://***/matlabcentral/fileexchange/158811-project1.
Learning-outcome prediction(LOP)is a long-standing and critical problem in educational *** studies have contributed to developing effective models while often suffering from data shortage and low generalization to var...
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Learning-outcome prediction(LOP)is a long-standing and critical problem in educational *** studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection *** this end,this study proposes a distributed grade prediction model,dubbed FecMap,by exploiting the federated learning(FL)framework that preserves the private data of local clients and communicates with others through a global generalized *** considers local subspace learning(LSL),which explicitly learns the local features against the global features,and multi-layer privacy protection(MPP),which hierarchically protects the private features,including model-shareable features and not-allowably shared features,to achieve client-specific classifiers of high performance on LOP per *** is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part,a local part,and a classification head in clients and averaging the global parts from clients on the *** evaluate the FecMap model,we collected three higher-educational datasets of student academic records from engineering *** results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP,compared with the state-of-the-art *** study makes a fresh attempt at the use of federated learning in the learning-analytical task,potentially paving the way to facilitating personalized education with privacy protection.
Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume an...
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Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume and limitations of computing, most existing traffic classification techniques are inapplicable to the high-speed network environment. In this paper, we propose a High-speed Encrypted Traffic Classification(HETC) method containing two stages. First, to efficiently detect whether traffic is encrypted, HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows. Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. The experimental results show that HETC can achieve a 94% F-measure in detecting encrypted flows and a 85%–93% F-measure in classifying fine-grained flows for a 1-KB flow-length dataset, outperforming the state-of-the-art comparison methods. Meanwhile, HETC does not need to wait for the end of the flow and can extract mass computing features. The average time for HETC to process each flow is only 2 or 16 ms, which is lower than the flow duration in most cases, making it a good candidate for high-speed traffic classification.
Since most multiobjective optimization problems in real-world applications contain constraints, constraint-handling techniques (CHTs) are necessary for a multiobjective optimizer. However, existing CHTs give no relaxa...
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Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics s...
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Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt *** results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.
This research proposes an Intelligent Decision Support System for Ground-Based Air Defense (GBAD) environments, which consist of Defended Assets (DA) on the ground that require protection from enemy aerial threats. A ...
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The development of the Space-Air-Ground Integrated Network (SAGIN) represents a significant advancement in satellite Internet technology, primarily because it facilitates the provision of pervasive networking solution...
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Modern large-scale computing systems always demand better connectivity indicators for reliability evaluation. However, as more processing units have been rapidly incorporated into emerging computing systems, existing ...
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Background: Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for near...
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