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11.
《Journal of the European Ceramic Society》2022,42(2):453-461
A few compositions of the system Sr2Mn1-xSnxO4 (x = 0.0, 0.3, 0.5) were synthesized in the air by the solid-state ceramic route. A change in the sign (positive to negative) of the permittivity above a particular temperature (TC) is observed at all the measured frequencies. The negative permittivity was analyzed by the Drude-Lorentz model. It was found that negative permittivity is caused by the plasma oscillations of thermally excited free charge carriers. Analysis of XPS spectra confirmed the presence of mixed-valence states of both Mn (Mn4+ and Mn3+) and Sn (Sn4+ and Sn2+) ions. The UV–vis.-IR spectroscopy results indicated generation of a large number of defect states in the forbidden bandgap region of Sr2MnO4 on the substitution of Sn at Mn site. Synthesized samples are promising metamaterials for radio frequency (10 Hz -2 MHz) region applications due to the high-temperature plasmonic behavior. 相似文献
12.
Tuladhar Sanira Alsadoon Abeer Prasad P. W. C. Ali Akbas Ezaldeen Alrubaie Ahmad 《Multimedia Tools and Applications》2022,81(17):23845-23865
Multimedia Tools and Applications - Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system... 相似文献
13.
The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems. 相似文献
14.
Varugeese Akshath Shahina A. Nawas Khadar Khan A. Nayeemulla 《Neural Processing Letters》2022,54(1):21-41
Neural Processing Letters - Transient Evoked Otoacoustic Emissions (TEOAE) are a class of oto-acoustic emissions that are generated by the cochlea in response to an external stimulus. The TEOAE... 相似文献
15.
Almuashi Mohammed Hashim Siti Zaiton Mohd Yusoff Nooraini Syazwan Khairul Nizar Ghabban Fahad 《Multimedia Tools and Applications》2022,81(27):39311-39342
Multimedia Tools and Applications - Analysis of facial images decoding familial features has been attracting the attention of researchers to develop a computerized system interested in determining... 相似文献
16.
Mohammed Amin Salih Venkatachalam K. Hubálovský S. Trojovský P. Prabu P. 《Mobile Networks and Applications》2022,27(3):1050-1059
Mobile Networks and Applications - 5G/6G communication are first generation high speed wireless communication network which integrates the aerial data, terrestrial data and maritime data via... 相似文献
17.
In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability. 相似文献
18.
Xiao Zhu Chen Yanxun Jiang Hongbo Hu Zhenzhen Lui John C. S. Min Geyong Dustdar Schahram 《Wireless Networks》2022,28(7):3305-3322
Wireless Networks - Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of... 相似文献
19.
Baig Md Amir Moinuddin Athar A. Khan E. Ghanbari M. 《Multimedia Tools and Applications》2022,81(6):7895-7916
Multimedia Tools and Applications - In this paper, blind image quality assessment (IQA) of Gaussian blurred images based on Discrete Fourier Transform (DFT) is proposed. The proposed work is based... 相似文献
20.
Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation. 相似文献