Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
Additive manufacturing (AM), often known as 3D printing, has emerged as a transformative technology with applications in a variety of industries, including the medical field. Traditional manufacturing techniques are l...
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Energy is crucial to progress toward development, modernization, and economic prosperity. Energy and water are both crucial to human survival and play significant roles in the growth and development of society. The ne...
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Recently,potassium-ion batteries(PIBs) have received significant attention in the energy storage field owing to their high-power output,fast charging capability,natural abundance,and environmental ***,we comprehensive...
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Recently,potassium-ion batteries(PIBs) have received significant attention in the energy storage field owing to their high-power output,fast charging capability,natural abundance,and environmental ***,we comprehensively review recent advancements in the design and development of carbon-based anode materials for PIBs anodes,covering graphite,hard carbon,alloy and conversion materials with carbon,and carbon host for K metal *** strategies such as structural engineering,heteroatom-doping,and surface modifications are highlighted to improve electrochemical performances as well as to resolve technical challenges,such as electrode instability,low initial Coulombic efficiency,and electrolyte ***,we discuss the fundamental understanding of potassium-ion storage mechanisms of carbon-based materials and their correlation with electrochemical ***,we present the current challenges and future research directions for the practical implementation of carbon-based anodes to enhance their potential as next-generation energy storage materials for *** review aims to provide our own insights into innovative design strategies for advanced PIB's anode through the chemical and engineering strategies.
In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational *** monitoring helps minimize unplanned downtime,extending equipment lifespan,re...
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In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational *** monitoring helps minimize unplanned downtime,extending equipment lifespan,reducing maintenance costs,and improving production quality and *** research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial *** study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature *** prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical assets.A data set of load information and vibration values from a heavy-duty industrial slip ring induction motor(4600 kW)and gearbox equipped with vibration sensors is used as a case *** study implements and compares six machine learning models with the proposed Bayesian-optimized stacked Long Short-Term Memory(LSTM)*** hyperparameters used in the implementation of models are selected based on the Bayesian optimization *** analysis reveals that the proposed Bayesian optimized stacked LSTM outperforms other models,showcasing its capability to learn temporal features as well as long-term dependencies in time series *** implemented machine learning models:Linear Regression(LR),RandomForest(RF),Gradient Boosting Regressor(GBR),ExtremeGradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Support Vector Regressor(SVR)displayed a mean squared error of 0.9515,0.4654,0.1849,0.0295,0.2127 and 0.0273,*** proposed model predicts the future vibration characteristics with a mean squared error of 0.0019 on the dataset containing motor load information and vibration *** results demonstrate that the proposed model outperforms other models in terms of other evaluation metrics wit
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced *** performance of existing CNN-based methods is limited by the puny general...
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Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced *** performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training *** the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention *** augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning *** proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and ***,the developed network is compared with other modern deep nets to check its *** addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also *** experimental results exhibited superior recognition accuracy of the proposed method compared to other existing *** addition,the developed method proves to be more effective and less sophisticated at extracting robust *** proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
The unconventional machining is an essential role in metal forming process. The dimensional accuracy and super finish are the main features of modern machining methods. Electric discharge machining is a competent proc...
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Environmental concerns promote demand for biodegradable packaging on a global scale. Jute fiber packaging could be a viable and sustainable alternative to pure synthetic materials. In this study, sustainable antimicro...
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Incremental sheet forming is a dieless forming process. Innovative analysis of deformations in the SPIF process, utilizing four distinct sets of deformed structures. Each set consists of four deformed shapes that are ...
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A transformer is an essential but expensive power delivery equipment for a distribution *** many distribution utilities worldwide,a sizable percentage of transformers are near the end of their designed *** the same ti...
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A transformer is an essential but expensive power delivery equipment for a distribution *** many distribution utilities worldwide,a sizable percentage of transformers are near the end of their designed *** the same time,distribution utilities are adopting smart inverter-based distributed solar photovoltaic(SPV)systems to maximize renewable *** central objective of this paper is to propose a methodology to quantify the effect of smart inverter-based distributed SPV systems on the aging of distribution *** proposed method is first tested on a modified IEEE-123 node distribution *** that,the procedure is applied to a practical distribution system,i.e.,the Indian Institute of technology(IIT)Roorkee campus,*** transformer aging models,alongside advanced control functionalities of grid-tied smart inverter-based SPV systems,are implemented in *** open-source simulation tool(OpenDSS)is used to model distribution *** analyze effectiveness of various inverter functionalities,time-series simulations are performed using exponential load models,considering daily load curves from multiple seasons,load types,current harmonics,*** show replacing a traditional inverter with a smart inverter-based SPV system can enable local reactive power generation and may extend the life of a distribution *** results demonstrate,simply by incorporating smart inverter-based SPV systems,transformer aging is reduced by 15%to 22%in comparison to SPV systems operating with traditional inverters.
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