Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks *** a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been p...
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Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks *** a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated ***,robust model-free control of robotic arms in the presence of noise interference remains a problem worth *** this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant ***,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic *** finite-time convergence and robustness of the proposed control scheme are proven by theoretical ***,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.
Deep learning models have been successfully adopted in anomaly detection for multivariate time series data in various fields. These models are good at capturing complex time dependencies and extracting meaningful patt...
Deep learning models have been successfully adopted in anomaly detection for multivariate time series data in various fields. These models are good at capturing complex time dependencies and extracting meaningful patterns from time series data. However, the trained models may become outdated due to unforeseen changes in real-world data, which can lead to a decrease in the quality of model service. Therefore, it is crucial to continuously monitor the performance of the model and analyze its behavior to ensure its reliability and availability. We propose an online data drift detection method that uses an unsupervised deep learning network, Variational Autoencoder (VAE), to monitor deep learning models in the field of multivariate time series anomaly detection. This method consists of three main steps namely data collection and statistical analysis, real-time drift detection, and drift interpretation. We collect raw time series data and model prediction data non-invasively from the model server. Then they are separated into windows for drift detection. Furthermore, the method can provide analysis and interpretation when drift is detected. Our evaluation experiments involve three real-world datasets from various industrial domains and four different structured anomaly detection models. We validate the effectiveness of drift detection in multivariate time series, and then test how the anomaly detection models perform during data drift detection. The highest improvement in F1 score is approximately 0.16. In addition, we provide an analysis of the interpretability of the model performance.
The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a c...
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ISBN:
(数字)9798350354539
ISBN:
(纸本)9798350354546
The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including Leaf Blight, Grey Leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops.
Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm "DenMu...
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The increasing number of Internet of Things (IoT) applications and their dependence on cloud computing for computational services has resulted in the cloud market’s growth. This growth has attracted many business org...
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Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due to the high dimensionality, data compl...
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This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the...
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Wireless local area network(WLAN)fingerprint-based localization has become the most attractive and popular approach for indoor ***,the primary concern for its practical implementation is the laborious manual effort of...
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Wireless local area network(WLAN)fingerprint-based localization has become the most attractive and popular approach for indoor ***,the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled *** Semi-supervised extreme learning machine(SELM) performs well in reducing calibration *** SELM methods only use Received signal strength(RSS) information to construct the neighbor graph and ignores location information,which helps recognizing prior information for manifold *** propose Composite SELM(CSELM) method by using both RSS signals and location information to construct composite ***,the issue of unlabeled RSS data quality has not been *** propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation(CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph,which considers location information in the semi-supervised extreme learning *** results show that the CSELM-QE could construct a precise localization model,reduce the calibration effort for radio map construction and improve localization *** quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.
In recent years, the impressive growth of new wireless technologies, together with the appearance of new requirements in applications and services, is progressively changing the use of networks. Due to the high mobili...
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Exploring the complementary information of multiview data to improve clustering effects is a crucial issue in multi-view clustering. In this paper, we propose a novel model based on information theory termed Informati...
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