Transmit Antenna Selection Based on SNR prediction in TDD Systems Using Convolutional Neural Network

Rapid quantitative analysis of cotton-polyester blended fabrics using near-infrared spectroscopy combined with CNN-LSTM

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

The uncertainty of wind power as the main obstacle of its integration into the power grid can be addressed by an accurate and efficient wind power forecast. Among the various wind power forecasting methods, machine learning (ML) algorithms, are recognized as a powerful wind power forecasting tool, however, their performance is highly dependent on the proper tuning of their hyperparameters. Common hyperparameter tuning methods such as grid search or random search are time-consuming, computationally expensive, and unreliable for complex models such as deep learning neural networks. Therefore, there is an urgent need for automatic methods to discover optimal hyperparameters for higher accuracy and efficiency of prediction models. In this study, a novel investigation is contributed to the field of wind power forecasting by a comprehensive comparison of three advanced techniques – Scikit-opt, Optuna, and Hyperopt – for hyperparameter optimization of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) models, a facet that, to our knowledge, has not been systematically explored in existing literature. The impact of these optimization techniques on the accuracy and efficiency of the CNN and LSTM models are assessed by comparing the root mean square error (RMSE) of the predictions and the required time to tune the models. The results show that the Optuna algorithm, using a Tree-structured Parzen Estimator (TPE) search method and Expected Improvement (EI) acquisition function, has the best efficiency for both CNN and LSTM models. In terms of accuracy, it is demonstrated that while for the CNN model all the optimization methods achieve similar performances, the LSTM model optimized by the Hyperopt algorithm, based on the annealing search method, results in the highest accuracy. In addition, for the first time in this research, the impact of the random initialization features on the performance of the forecasting models with neural networks is investigated. The proposed structures for deep learning models were examined to determine the most robust structure with the minimal sensitivity to the randomness. What we have discovered from the comparison of advanced hyperparameter optimization methods can be used by researchers to tune the time series-based forecasting models.

In materials science, high-throughput material processing and testing are crucial for rapid materials design and discovery, but manual operations often create bottlenecks in terms of speed and accuracy. Automating the repetitive and labor-intensive aspects of material testing significantly increases throughput and consistency, facilitating a more efficient pathway to material innovation. This study demonstrates automated process control by integrating robotic automation in a high-throughput laser shock system using closed-loop “see-move-shoot” experiments. The system employs two automated linear stages to sequentially manipulate material specimens (flyers) under a laser for impact testing. Each experiment includes automatic target detection, sample centering, laser activation, impact verification, and progression to the next sample. For flyer detection, we developed a computer vision algorithm using a convolutional neural network (CNN) model based on EfficientNetB0. Trained on 16,000 labeled images under various lighting conditions, the model achieved a root mean square error (RMSE) of 0.038 mm in extreme testing conditions (i.e., under high exposure, low light, or blurry images) ensuring reliable and efficient real-time processing. In our “see-move-shoot” comparison study, manual operation took 30.6 seconds for a novice user and 19.2 seconds for an expert user per shot, while the CNN model required only 7.46 seconds. Consequently, the CNN model conducts experiments 4 times faster than the novice user and 2.5 times faster than the expert user.

In order to cure the crux of low accuracy of traditional PG transient stability prediction (PGTSP) methods, a PGTSP method based on improved Convolutional Neural Network(CNN) is proposed under big data. First, the theoretical framework of state assessment of Power Grid (PG) is constructed according to analysis of historical big data in PG. Based on data source, big data classification, big data cleaning and processing, PG status assessment is realized. Then, by selecting a variety of PG traits as the multi-input trait space of CNN model. By using multi-channel idea to independently analyze and fuse various features, a multi-channel multi-trait fusion CNN(MC-MF-FCNN) model is constructed and the accurate prediction of grid transient stability is achieved. Finally, the root mean square error(RMSE), false alarm rate, false alarm rate and accuracy rate of the proposed algorithm and the other two algorithms under the same conditions are compared and analyzed through simulation experiments. The results show that RMSE, false alarm rate and miss alarm rate of the proposed algorithm are the smallest and the accuracy is the highest. The highest accuracy rate within 9 cycles after the fault is 96.83 %, and the minimum RMSE, missed alarm rate and false alarm rate are 0.196, 2.15% and 1.32%, respectively. The performance is better than the other two comparison algorithms.

Particulate phosphorus (PP) is a major contributor to lakes developing eutrophic. It also serves as one of the most significant sources of phosphorus for primary productivity, serving as a possible source of soluble reactive phosphorus, and contributing a sizable amount of the total phosphorus (TP), so monitoring the spatial and temporal variability of PP is crucial for understanding eutrophication in water bodies. This study aims to propose an algorithm to accurately predict the PP concentration and to assess the performance of the model. Considering Chaohu Lake as a case study, we proposed a random forest algorithm based on the convolutional neural network (CNN‐RF) to investigate the spatial and temporal patterns of PP concentration in the lake using HJ‐2 hyperspectral satellite images. The applicability of backpropagation (BP) neural network, random forest (RF), convolutional neural network (CNN), and CNN‐RF models for remote sensing inversion of PP concentration is assessed through model comparison. The results indicate that the CNN‐RF model has the best prediction performance, with a coefficient of determination (R2) of 0.91, a residual prediction deviation (RPD) of 3.42, a root mean square error (RMSE) of 0.0155 mg/L, and a percentage bias (PBIAS) of 5.01%, which is better than the BP model by 42%, 106%, 51%, and 81%, the RF model by 25%, 77%, 44%, and 59%, and the CNN model by 9.6%, 42%, 30%, and 49%, respectively, and 59% better than the CNN model. The inversion findings suggest that the PP of Chaohu Lake is substantially high in summer and autumn but comparatively low in spring and winter. The spatial distribution demonstrates an arrangement of highest in the western part of the lake, second in the eastern region, and lowest in the central, with obvious spatial and temporal distribution characteristics, and the trend is closely related to the quarterly mean air temperature and mean precipitation. This indicates that based on the differences in phosphorescence scattering signals of different morphologies in water bodies, the use of hyperspectral remote sensing and the CNN‐RF model can effectively extract PP spatiotemporal information, strengthen the learning capability of multiscale characteristics, and contribute to the improvement of the precision of estimating PP concentration, which could provide an innovative approach for determining the degree of eutrophication of lake water bodies.

In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (Es), demonstrating an improvement over current methods. This includes a binary classification model to determine if Es is present, followed by a regression model to estimate the Es ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the Es layer occurs (hEs). Signal‐to‐noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground‐based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1‐score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root‐mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known that Es was present. When combining the classification and regression models together for use in practical applications where it is unknown if Es is present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

ObjectiveThis study aimed to develop and validate an Informer- Convolutional Neural Network (CNN) model to predict the gamma passing rate (GPR) for patient-specific quality assurance in volumetric modulated arc therapy (VMAT), enhancing treatment safety and efficacy by integrating multiple data sources.MethodsAnalyzing 465 VMAT treatment plans covering head & neck, chest, and abdomen, the study extracted data from 31 complexity indicators, 123 radiomics features, and 123 dosimetrics indices, along with daily linac performance data including 141 key performance indicators. A hybrid Informer-CNN architecture was used to handle both temporal and non-temporal data for predicting GPR.ResultsThe Informer-CNN model demonstrated superior predictive performance over traditional models like Convolutional Neural Networks (CNN), Long Short-Term Memory(LSTM), and Informer. Specifically, in the validation set, the model achieved a mean absolute error (MAE) of 0.0273 and a root mean square error (RMSE) of 0.0360 using the 3%/3mm criterion. In the test set, the MAE was 0.0327 and the RMSE was 0.0468. The model also showed high classification performance with AUC scores of 0.97 and 0.95 in test and validation sets, respectively.ConclusionThe developed Informer-CNN model significantly enhances the prediction accuracy and classification of gamma passing rates in VMAT treatment plans. It facilitates early integration of daily accelerator performance data, improving the assessment and verification of treatment plans for better patient-specific quality assurance.

Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 were selected for model training and testing. First, CNN was implemented to extract key meteorological and pollution data. Subsequently, the RF algorithm was employed to train the model with five input factors, namely the extracted features from the CNN and spatiotemporal factors, including the day of the year, the hour of the day, latitude, and longitude. Independent observations from two stations were used to evaluate the models. The findings demonstrated that the proposed CNN-RF model had better modeling capability compared with the independent CNN and RF models: the average improvements in root mean square error (RMSE) and mean absolute error (MAE) ranged from 8.10% to 11.11%, respectively. In addition, the proposed CNN-RF hybrid model has fewer excess residuals at thresholds of 10 μg/m3, 20 μg/m3, and 30 μg/m3. The results revealed that the proposed CNN-RF ensemble framework is a stable, reliable, and accurate method that can generate superior results compared with the single CNN and RF methods. The proposed method could be a valuable reference for readers and may inspire researchers to develop even more effective methods for air pollution modeling. This research has important implications for air pollution research, data analysis, model estimation, and machine learning.

In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from the covariance matrix of the received signal, the signal-to-noise ratio (SNR) can be increased by up to 5 dB by the cyclic noise reduction method, which will improve the DOA estimation accuracy. The improved CAPON algorithm has a convolution neural network (CNN) structure, whose input is the processed covariance matrix of the received signal, and the CAPON spectral value is used as the training label to obtain the estimated spatial spectrum. It retains the advantages of the CAPON algorithm, which can achieve blind source estimation, and simulations show that the proposed algorithm exhibits a better performance than the traditional algorithm in conditions of various SNRs and snapshot numbers. The simulation results show that, based on a certain SNR, the root mean square error (RMSE) of the improved CAPON algorithm can be reduced from 0.86° to 0.8° compared to traditional algorithms, and the angle estimation error can be decreased by up to about 0.3°. With the help of the cyclic noise reduction method, the angle estimation error decreases from 0.04° to 0.02°.

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.

Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia

Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks