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Journal articles on the topic 'Multi-channel linear prediction'
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Jukic, Ante, Toon van Waterschoot, Timo Gerkmann, and Simon Doclo. "Multi-Channel Linear Prediction-Based Speech Dereverberation With Sparse Priors." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 9 (September 2015): 1509–20. http://dx.doi.org/10.1109/taslp.2015.2438549.
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Mousavi, Leila, Farbod Razzazi, and Afrooz Haghbin. "Blind speech dereverberation using sparse decomposition and multi-channel linear prediction." International Journal of Speech Technology 22, no. 3 (July 15, 2019): 729–38. http://dx.doi.org/10.1007/s10772-019-09620-x.
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DELCROIX, M., T. HIKICHI, and M. MIYOSHI. "On a Blind Speech Dereverberation Algorithm Using Multi-Channel Linear Prediction." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 10 (October 1, 2006): 2837–46. http://dx.doi.org/10.1093/ietfec/e89-a.10.2837.
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Park, Sangwoo, and Osvaldo Simeone. "Speeding Up Training of Linear Predictors for Multi-Antenna Frequency-Selective Channels via Meta-Learning." Entropy 24, no. 10 (September 26, 2022): 1363. http://dx.doi.org/10.3390/e24101363.
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An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel-prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.
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Delcroix, Marc, Takafumi Hikichi, and Masato Miyoshi. "Blind dereverberation algorithm for speech signals based on multi-channel linear prediction." Acoustical Science and Technology 26, no. 5 (2005): 432–39. http://dx.doi.org/10.1250/ast.26.432.
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Wang, Ning, and Jia-Yang Li. "Efficient Multi-Channel Thermal Monitoring and Temperature Prediction Based on Improved Linear Regression." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–9. http://dx.doi.org/10.1109/tim.2021.3139659.
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Yoshioka, Takuya, and Tomohiro Nakatani. "Generalization of Multi-Channel Linear Prediction Methods for Blind MIMO Impulse Response Shortening." IEEE Transactions on Audio, Speech, and Language Processing 20, no. 10 (December 2012): 2707–20. http://dx.doi.org/10.1109/tasl.2012.2210879.
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Jin, Xin, Xin Liu, Jinyun Guo, and Yi Shen. "Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction." Sensors 21, no. 4 (February 17, 2021): 1403. http://dx.doi.org/10.3390/s21041403.
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Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.
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Yuxuan Zhou, Yuxuan Zhou, Wanzhong Chen Yuxuan Zhou, Linlin Li Wanzhong Chen, Linlin Gong Linlin Li, and Chang Liu Linlin Gong. "The Energy-Efficient Resource Allocation of Multi-Modal Perception for Affective Brain-Computer Interactions Based on Non-Linear Iterative Prediction Scheme." 網際網路技術學刊 24, no. 3 (May 2023): 641–50. http://dx.doi.org/10.53106/160792642023052403009.
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<p>For the whole environmental settings in this research, the conventional affective brain-computer interactions can not build a good performance on energy-efficient resource of network’s forwarding ports and routing paths due to its poor allocation function of cognitive radio networks, based on the novel interactive networking architecture, the model of non-linear iterative prediction scheme in interaction was successively proposed. This research proposes a modified LSTM algorithm with a structure of non-linear iterative in complexity prediction, joins the multiple k modes selection and multi-agent systems, maximizes EERA of forwarding and routing while maintaining the communication quality. Firstly, considering whether this affective brain-computer interactions need the networking communication in system. Secondly, adjusting the forwarding and routing factors of energy-efficient resource allocation by selecting the best optimal energy-efficient resource for the links through the non-linear iterative prediction in a multi-modal perception. The simulation results show that compared with the other models and algorithms, the proposed scheme for affective brain-computer interactions, which has a nice performance on a higher EERA and channel utilization of a networking architecture of brain-computer interactions.</p> <p> </p>
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Lv, X., C. Jing, Y. Wang, and S. Jin. "A DEEP NEURAL NETWORK FOR SPATIOTEMPORAL PREDICTION OF THEFT CRIMES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-3/W2-2022 (October 27, 2022): 35–41. http://dx.doi.org/10.5194/isprs-archives-xlviii-3-w2-2022-35-2022.
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Abstract. Accurate crime prediction plays an important role in public safety, providing technical guidance and decision support for the police and government departments. Due to the dynamics and imbalance of crime distribution, it is difficult to build predictive models for it. Specifically, the fine-grained and non-linear spatiotemporal dependencies of crime data cannot be captured accurately. In this paper, a neural network model ST-ACLCrime based on ConvLSTM and SE block was proposed to predict the number of theft crimes in hotspot areas. By overlaying ConvLSTM layers, fine-grained spatiotemporal dependencies are captured while preserving spatial location information. To further enhance the global channel feature representation, SE block is used to recalibrate the channel features and enhance the channel inter-dependencies. In addition, the closeness and the period components are set to dynamically capture the dependence of different time trends. We choose the city of Chicago as the study case, and use a multi-level spatial grid to divide the whole city area. The experimental results show that the proposed model exceeds all baseline model, such as HA, CNN, LSTM, CNN-LSTM and ConvLSTM. It was effectively capturing spatiotemporal dependence and improving prediction accuracy.
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Jang, Jae Kyeong, Haider Abbas, and Jung Ruyl Lee. "Development of Multi-channel Simultaneous Laser Shock Sensing System for Linear Explosive-induced Pyroshock Propagation Prediction." Journal of the Korean Society of Propulsion Engineers 19, no. 5 (October 1, 2015): 46–51. http://dx.doi.org/10.6108/kspe.2015.19.5.046.
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Chen, Xiaolei, Hao Chang, Baoning Cao, Yubing Lu, and Dongmei Lin. "Prediction of Continuous Blood Pressure Using Multiple Gated Recurrent Unit Embedded in SENet." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 2 (March 20, 2022): 256–63. http://dx.doi.org/10.20965/jaciii.2022.p0256.
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In order to accurately predict blood pressure waveform from pulse waveform, a multiple gated recurrent unit (GRU) model embedded in squeeze-and-excitation network (SENet) is proposed for continuous blood pressure prediction. Firstly, the features of the pulse are extracted from multiple GRU channels. Then, the SENet module is embedded to learn the interdependence among the channels, so as to get the weight of each channel. Finally, the weights were added to each channel and the predicted continuous blood pressure values were obtained by integrating the two linear layers. The experimental results show that the embedded SENet can effectively enhance the predictive ability of multi-GRU structure and obtain good continuous blood pressure waveform. Compared with the LSTM and GRU model without SENet, the MSE errors of the proposed method are reduced by 29.3% and 25.0% respectively, the training time of the proposed method are decreased by 69.8% and 68.7%, the test time is reduced by 65.9% and 25.2% and it has the fewest model parameters.
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Li, Bian, Yucheng T. Yang, John A. Capra, and Mark B. Gerstein. "Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks." PLOS Computational Biology 16, no. 11 (November 30, 2020): e1008291. http://dx.doi.org/10.1371/journal.pcbi.1008291.
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Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.
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Dietzen, Thomas, Ann Spriet, Wouter Tirry, Simon Doclo, Marc Moonen, and Toon van Waterschoot. "Comparative Analysis of Generalized Sidelobe Cancellation and Multi-Channel Linear Prediction for Speech Dereverberation and Noise Reduction." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 3 (March 2019): 544–58. http://dx.doi.org/10.1109/taslp.2018.2886743.
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Cui, Hua Chao, An Bang Zhao, Bin Zhou, Kun Ping Sun, and Yan Cui. "Implementation and Design of Underwater Duplex Speech Communication System Based on Vector Hydrophone." Applied Mechanics and Materials 336-338 (July 2013): 1939–44. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1939.
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This paper realizes 1.2kbps Mixed Excitation Linear Prediction (MELP) speech compression algorithm through multi-frame joint and Vector Quantization technology. In order to adapt to underwater acoustic channel, we apply the Orthogonal Frequency Division Modulation (OFDM) to modulate the source bits, together with synchronization, channel estimation and RS error correcting code technology. The hardware platform of this system relies on TMS320DM642 and AIC23. Moreover, we introduce single vector hydrophone to restrain the local source. The system finished duplex speech signal acquisition and transmission and tank test results prove that the synthesized speech is clear and understandable and it has a stable performance.
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Jiang, Benchi, Shilei Bian, Chenyang Shi, and Lulu Wu. "Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion." Applied Sciences 13, no. 15 (July 28, 2023): 8760. http://dx.doi.org/10.3390/app13158760.
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This study focuses on improving the objective alignment of image quality assessment (IQA) algorithms with human visual perception. Existing methodologies, predominantly those based on the Laplacian of Gaussian (LoG) filter, often neglect the impact of color channels on human visual perception. Consequently, we propose a full-reference IQA method that integrates multi-channel visual information in color images. The methodology begins with converting red, green, blue (RGB) images into the luminance (L), red–green opponent color channel (M), blue–yellow opponent color channel (N) or LMN color space. Subsequently, the LoG filter is separately applied to the L, M, and N channels. The convoluted components are then fused to generate a contrast similarity map using the root-mean-square method, while the chromaticity similarity map is derived from the color channels. Finally, multi-channel LoG filtering, contrast, and chromaticity image features are connected. The standard deviation method is then used for sum pooling to create a full-reference IQA computational method. To validate the proposed method, distorted images from four widely used image databases were tested. The evaluation, based on four criteria, focused on the method’s prediction accuracy, computational complexity, and generalizability. The Pearson linear correlation coefficient (PLCC) values, recorded from the databases, ranged from 0.8822 (TID2013) to 0.9754 (LIVE). Similarly, the Spearman rank-order correlation coefficient (SROCC) values spanned from 0.8606 (TID2013) to 0.9798 (LIVE). In comparison to existing methods, the proposed IQA method exhibited superior visual correlation prediction accuracy, indicating its promising potential in the field of image quality assessment.
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Yu, Liyi, Zhaochun Xu, Meiling Cheng, Weizhong Lin, Wangren Qiu, and Xuan Xiao. "MSEDDI: Multi-Scale Embedding for Predicting Drug—Drug Interaction Events." International Journal of Molecular Sciences 24, no. 5 (February 24, 2023): 4500. http://dx.doi.org/10.3390/ijms24054500.
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A norm in modern medicine is to prescribe polypharmacy to treat disease. The core concern with the co-administration of drugs is that it may produce adverse drug—drug interaction (DDI), which can cause unexpected bodily injury. Therefore, it is essential to identify potential DDI. Most existing methods in silico only judge whether two drugs interact, ignoring the importance of interaction events to study the mechanism implied in combination drugs. In this work, we propose a deep learning framework named MSEDDI that comprehensively considers multi-scale embedding representations of the drug for predicting drug—drug interaction events. In MSEDDI, we design three-channel networks to process biomedical network-based knowledge graph embedding, SMILES sequence-based notation embedding, and molecular graph-based chemical structure embedding, respectively. Finally, we fuse three heterogeneous features from channel outputs through a self-attention mechanism and feed them to the linear layer predictor. In the experimental section, we evaluate the performance of all methods on two different prediction tasks on two datasets. The results show that MSEDDI outperforms other state-of-the-art baselines. Moreover, we also reveal the stable performance of our model in a broader sample set via case studies.
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Li, Ting Ting, Zhao Wang, Shi Zhong Ma, and Zhao Wang. "The Application of Seismic Sedimentology in Prediction of Channel Sand in Pu I Formation, Gaotaizi Oilfield." Advanced Materials Research 734-737 (August 2013): 1476–79. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.1476.
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Pu I formation in Gaotaizi oilfield mainly developed delta distributary channels. The sand body is narrow and has a complex superimposition in vertical, which caused the sand body hard to forecast. To solve this problem the method of seismic sedimentology was applied to building high frequency stratigraphic framework and extracting high-precision stratal slice. The technique of 90-degree phasing conversion and multi-attribute comprehensive analysis to the channel sand was also performed. At last Pu I formation in Gaotaizi Oilfield is divided into fifteen fifth-order sequences. The attributes of Rms amplitude and Arc length are considered as the best attribute to reflect the characteristics of the channel sand in study area. By a multiple linear regression to the attributes, the prediction accuracy between wells is improved from 61% to 78%.
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Morales, Giorgio, John W. Sheppard, Paul B. Hegedus, and Bruce D. Maxwell. "Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing." Sensors 23, no. 1 (January 2, 2023): 489. http://dx.doi.org/10.3390/s23010489.
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In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further improve the yield prediction task by using Convolutional Neural Networks (CNNs) given their unique ability to exploit the spatial information of small regions of the field. We present a novel CNN architecture called Hyper3DNetReg that takes in a multi-channel input raster and, unlike previous approaches, outputs a two-dimensional raster, where each output pixel represents the predicted yield value of the corresponding input pixel. Our proposed method then generates a yield prediction map by aggregating the overlapping yield prediction patches obtained throughout the field. Our data consist of a set of eight rasterized remotely-sensed features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), aspect, and two radar backscatter coefficients acquired from the Sentinel-1 satellites. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present leave-one-out cross-validation experiments for rain-fed winter wheat over four fields and show that our proposed methodology produces better predictions than five compared methods, including Bayesian multiple linear regression, standard multiple linear regression, random forest, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.
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Sheintuch, L., A. Friedman, N. Efrat, C. Tifeeret, Z. Shorer, I. Neuman, and I. Shallom. "O16: Detection of epileptiform activity using multi-channel linear prediction coefficients and localization of epileptic foci based on EEG-fMRI data." Clinical Neurophysiology 125 (June 2014): S33. http://dx.doi.org/10.1016/s1388-2457(14)50122-8.
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Ji, Huiwen, Min Xia, Dongsheng Zhang, and Haifeng Lin. "Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection." ISPRS International Journal of Geo-Information 12, no. 6 (June 18, 2023): 247. http://dx.doi.org/10.3390/ijgi12060247.
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Cloud and cloud shadow detection are essential in remote sensing imagery applications. Few semantic segmentation models were designed specifically for clouds and their shadows. Based on the visual and distribution characteristics of clouds and their shadows in remote sensing imagery, this paper provides a multi-supervised feature fusion attention network. We design a multi-scale feature fusion block (FFB) for the problems caused by the complex distribution and irregular boundaries of clouds and shadows. The block consists of a fusion convolution block (FCB), a channel attention block (CAB), and a spatial attention block (SPA). By multi-scale convolution, FCB reduces excessive semantic differences between shallow and deep feature maps. CAB focuses on global and local features through multi-scale channel attention. Meanwhile, it fuses deep and shallow feature maps with non-linear weighting to optimize fusion performance. SPA focuses on task-relevant areas through spatial attention. With the three blocks above, FCB alleviates the difficulties of fusing multi-scale features. Additionally, it makes the network resistant to background interference while optimizing boundary detection. Our proposed model designs a class feature attention block (CFAB) to increase the robustness of cloud detection. The network achieves good performance on the self-made cloud and shadow dataset. This dataset is taken from Google Earth and contains remote sensing imagery from several satellites. The proposed model achieved a mean intersection over union (MIoU) of 94.10% on our dataset, which is 0.44% higher than the other models. Moreover, it shows high generalization capability due to its superior prediction results on HRC_WHU and SPARCS datasets.
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Jia, Hongwei, Haiyong Luo, Hao Wang, Fang Zhao, Qixue Ke, Mingyao Wu, and Yunyun Zhao. "ADST: Forecasting Metro Flow Using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning." Sensors 20, no. 16 (August 14, 2020): 4574. http://dx.doi.org/10.3390/s20164574.
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Passenger flow prediction has drawn increasing attention in the deep learning research field due to its great importance in traffic management and public safety. The major challenge of this essential task lies in multiple spatiotemporal correlations that exhibit complex non-linear correlations. Although both the spatial and temporal perspectives have been considered in modeling, most existing works have ignored complex temporal correlations or underlying spatial similarity. In this paper, we identify the unique spatiotemporal correlation of urban metro flow, and propose an attention-based deep spatiotemporal network with multi-task learning (ADST-Net) at a citywide level to predict the future flow from historical observations. ADST-Net uses three independent channels with the same structure to model the recent, daily-periodic and weekly-periodic complicated spatiotemporal correlations, respectively. Specifically, each channel uses the framework of residual networks, the rectified block and the multi-scale convolutions to mine spatiotemporal correlations. The residual networks can effectively overcome the gradient vanishing problem. The rectified block adopts an attentional mechanism to automatically reweigh measurements at different time intervals, and the multi-scale convolutions are used to extract explicit spatial relationships. ADST-Net also introduces an external embedding mechanism to extract the influence of external factors on flow prediction, such as weather conditions. Furthermore, we enforce multi-task learning to utilize transition passenger flow volume prediction as an auxiliary task during the training process for generalization. Through this model, we can not only capture the steady trend, but also the sudden changes of passenger flow. Extensive experimental results on two real-world traffic flow datasets demonstrate the obvious improvement and superior performance of our proposed algorithm compared with state-of-the-art baselines.
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Gao, Hongming, Hongwei Liu, Haiying Ma, Cunjun Ye, and Mingjun Zhan. "Network-aware credit scoring system for telecom subscribers using machine learning and network analysis." Asia Pacific Journal of Marketing and Logistics 34, no. 5 (October 5, 2021): 1010–30. http://dx.doi.org/10.1108/apjml-12-2020-0872.
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PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.
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Gao, Hongming, Hongwei Liu, Haiying Ma, Cunjun Ye, and Mingjun Zhan. "Network-aware credit scoring system for telecom subscribers using machine learning and network analysis." Asia Pacific Journal of Marketing and Logistics 34, no. 5 (October 5, 2021): 1010–30. http://dx.doi.org/10.1108/apjml-12-2020-0872.
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PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.
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Sun, Dajun, Jie Wu, Xiaoping Hong, Changxin Liu, Hongyu Cui, and Boyu Si. "Iterative double-differential direct-sequence spread spectrum reception in underwater acoustic channel with time-varying Doppler shifts." Journal of the Acoustical Society of America 153, no. 2 (February 2023): 1027–41. http://dx.doi.org/10.1121/10.0017116.
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Conventional double differential phase-shift keying modulation amplifies the phase noise and performs poorly under the time-varying direct-sequence spread-spectrum (DSSS) communication system. Therefore, the authors propose an iterative reception for DSSS communication in time-varying underwater acoustic channels. First, bit-interleaved coded modulation with iterative decoding integrated with multi-symbol differential detection is used. Second, this paper uses cross correlation method to estimate and track the Doppler shift of each symbol. Based on Doppler estimates, a dynamic linear prediction model is proposed to estimate and track the channel phase variation. Third, an algorithm for adaptive selection of reference signals is utilized to recover the magnitude attenuation of correlation peaks. Numerical simulation results demonstrate that the proposed reception achieves around 9 dB gain compared to conventional differential decision reception under constant acceleration of 0.14 [Formula: see text]. During the acoustic communication experiment in Songhua Lake, the proposed reception was tested by using a moving source at a speed of 1–6 knots at 2-m depth and the farthest distance between the transceivers is 2.8 km. The proposed reception achieves only one frame error from a total of 205 frames collected in the lake experiment, and it also achieves error-free communications over 96 frames during a 10 km depth deep-sea experiment.
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Schalk, Robert, Annabell Heintz, Frank Braun, Giuseppe Iacono, Matthias Rädle, Norbert Gretz, Frank-Jürgen Methner, and Thomas Beuermann. "Comparison of Raman and Mid-Infrared Spectroscopy for Real-Time Monitoring of Yeast Fermentations: A Proof-of-Concept for Multi-Channel Photometric Sensors." Applied Sciences 9, no. 12 (June 17, 2019): 2472. http://dx.doi.org/10.3390/app9122472.
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Raman and mid-infrared (MIR) spectroscopy are useful tools for the specific detection of molecules, since both methods are based on the excitation of fundamental vibration modes. In this study, Raman and MIR spectroscopy were applied simultaneously during aerobic yeast fermentations of Saccharomyces cerevisiae. Based on the recorded Raman intensities and MIR absorption spectra, respectively, temporal concentration courses of glucose, ethanol, and biomass were determined. The chemometric methods used to evaluate the analyte concentrations were partial least squares (PLS) regression and multiple linear regression (MLR). In view of potential photometric sensors, MLR models based on two (2D) and four (4D) analyte-specific optical channels were developed. All chemometric models were tested to predict glucose concentrations between 0 and 30 g L−1, ethanol concentrations between 0 and 10 g L−1, and biomass concentrations up to 15 g L−1 in real time during diauxic growth. Root-mean-squared errors of prediction (RMSEP) of 0.68 g L−1, 0.48 g L−1, and 0.37 g L−1 for glucose, ethanol, and biomass were achieved using the MIR setup combined with a PLS model. In the case of Raman spectroscopy, the corresponding RMSEP values were 0.92 g L−1, 0.39 g L−1, and 0.29 g L−1. Nevertheless, the simple 4D MLR models could reach the performance of the more complex PLS evaluation. Consequently, the replacement of spectrometer setups by four-channel sensors were discussed. Moreover, the advantages and disadvantages of Raman and MIR setups are demonstrated with regard to process implementation.
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Wu, Peng, Gongye Yu, Naiji Dong, and Bo Ma. "Acoustic Feature Extraction Method of Rotating Machinery Based on the WPE-LCMV." Machines 10, no. 12 (December 6, 2022): 1170. http://dx.doi.org/10.3390/machines10121170.
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Fault diagnosis plays an important role in the safe and stable operation of rotating machinery, which is conducive to industrial development and economic improvement. However, effective feature extraction of rotating machinery fault diagnosis is difficult in the complex sound field with characteristics of reverberation and multi-dimensional signals. Therefore, this paper proposes a novel acoustic feature extraction method of the rotating machinery based on the Weighted Prediction Error (WPE) integrating the Linear Constrained Minimum Variance (LCMV). The de-reverberation signal is obtained by inputting multi-channel signals into the WPE algorithm using an adaptive optimal parameters selection function with the sound field changes. Then, the incident angle going from the fault source to the center of the microphone array is calculated from the full-band sound field distribution, and the signal is de-noised and fused using the LCMV. Finally, the fault feature frequency is extracted from the fused signal envelope spectrum. The results of fault data analysis of the centrifugal pump test bench show that the Envelope Harmonic Noise Ratio (EHNR) is more than twice that of the original signal after the WPE-LCMV processing. Compared to the Recursive Least Squares and the Resonance Sparse Signal Decomposition (RLS-RSSD) and the parameter optimized Variational Mode Decomposition (VMD), the EHNR has a higher value for all types of faults after applying the WPE-LCMV processing. Furthermore, the proposed method can effectively extract the frequency of bearing faults.
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Liu, Jun, Renfu Li, Yuxuan Chen, Jianguo Zheng, and Kun Wang. "Topology Optimization Method of a Cavity Receiver and Built-In Net-Based Flow Channels for a Solar Parabolic Dish Collector." Entropy 25, no. 3 (February 22, 2023): 398. http://dx.doi.org/10.3390/e25030398.
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The design of a thermal cavity receiver and the arrangement of the fluid flow layout within it are critical in the construction of solar parabolic dish collectors, involving the prediction of the thermal–fluid physical field of the receiver and optimization design. However, the thermal–fluid analysis coupled with a heat loss model of the receiver is a non-linear and computationally intensive solving process that incurs high computational costs in the optimization procedure. To address this, we implement a net-based thermal–fluid model that incorporates heat loss analysis to describe the receiver’s flow and heat transfer processes, reducing computational costs. The physical field results of the net-based thermal–fluid model are compared with those of the numerical simulation, enabling us to verify the accuracy of the established thermal–fluid model. Additionally, based on the developed thermal–fluid model, a topology optimization method that employs a genetic algorithm (GA) is developed to design the cavity receiver and its built-in net-based flow channels. Using the established optimization method, single-objective and multi-objective optimization experiments are conducted under inhomogeneous heat flux conditions, with objectives including maximizing temperature uniformity and thermal efficiency, as well as minimizing the pressure drop. The results reveal varying topological characteristics for different optimization objectives. In comparison with the reference design (spiral channel) under the same conditions, the multi-objective optimization results exhibit superior comprehensive performance.
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Goosmann, Tobias, Sebastian Raab, Philipp Oppek, Andre Weber, and Ellen Ivers-Tiffee. "Impedance-Based, Multi-Physical DC-Performance-Model for a PEMFC Stack." ECS Meeting Abstracts MA2022-01, no. 46 (July 7, 2022): 1959. http://dx.doi.org/10.1149/ma2022-01461959mtgabs.
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Performance prediction for large-sized polymer electrolyte membrane fuel cell (PEMFC) stacks necessitates consideration of spatially deviating operating conditions on the nonlinear electrochemical behaviour. This interaction between operating conditions and electrochemistry is best described by complex CFD-models. But high computing power excludes stack and system modelling in real time applications. We address this challenge by a multi-physical stack model, which couples (i) the non-linear electrochemistry within the cell, (ii) the fluid pressure drop along the gas channels and (iii) the thermal behaviour within the stack. The spatial resolution focusses on the most relevant directions and thus limits the computational effort. Simulation runtime is further reduced by modelling the electrochemical behaviour by a physico-chemically meaningful equivalent circuit model (ECM) [1], which relies on a data set of electrochemical impedance spectroscopy (EIS) measurements performed on incremental cells [2]. Individual impedance contributions are identified by the distribution of relaxation times (DRT). ECM model parameters are subsequently quantified by a CNLS-fitting procedure [3,4] and transferred to a nonlinear, zero-dimensional DC-performance-model. The magnitude of the modelled pressure depends on the gas flow within the channel and considers the change of gas composition, whereas the local gradients in current density cause gradients in released heat within the cell itself. This effect along the gas flow, the convection between fluids and solid parts of the stack (bipolar plates and cell) and the internal heat conduction between the solid control volumes are considered in the modelled thermal behaviour. In this contribution a multi-physical stack model considering gradients in temperature, pressure and gas composition is presented. The interdisciplinary interactions and dependencies within the different physical domains, especially the influence of pressure and temperature on the non-linear electrochemical model, are shown. A concise validation based on measured data and conclusions for the possibilities of further applications as a system model will be discussed. [1] D. Klotz et. al., ECS Transactions 25, pp. 1331-1340 (2009) [2] M. Heinzmann et al., J. Power Sources 402, pp. 24-33 (2018). [3] H. Schichlein et al., J. Appl. Electrochem. 32, pp. 875-882 (2002). [4] S. Dierickx et al. Electrochimica Acta 355, 136764 (2020)
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Cao, Zhenguan, Liao Fang, Zhuoqin Li, and Jinbiao Li. "Lightweight Target Detection for Coal and Gangue Based on Improved Yolov5s." Processes 11, no. 4 (April 19, 2023): 1268. http://dx.doi.org/10.3390/pr11041268.
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The detection of coal and gangue is an essential part of intelligent sorting. A lightweight coal and gangue detection algorithm based on You Only Look Once version 5s (Yolov5s) is proposed for the current coal and gangue target detection algorithm with the low accuracy of small target detection, high model complexity, and sizeable computational memory consumption. Firstly, we build a new convolutional block based on the Funnel Rectified Linear Unit (FReLU) activation function and apply it to the original Yolov5s network so that the model adaptively captures local contextual information of the image. Secondly, the neck of the original network is redesigned to improve the detection accuracy of small samples by adding a small target detection head to achieve multi-scale feature fusion. Next, some of the standard convolution modules in the original network are replaced with Depthwise Convolution (DWC) and Ghost Shuffle Convolution (GSC) modules to build a lightweight feature extraction network while ensuring the model detection accuracy. Finally, an efficient channel attention (ECA) module is embedded in the backbone of the lightweight network to facilitate accurate localization of the prediction region by improving the information interaction of the model with the channel features. In addition, the importance of each component is fully demonstrated by ablation experiments and visualization analysis comparison experiments. The experimental results show that the mean average precision (mAP) and the model size of our proposed model reach 0.985 and 4.9 M, respectively. The mAP is improved by 0.6%, and the number of parameters is reduced by 72.76% compared with the original Yolov5s network. The improved algorithm has higher localization and recognition accuracy while significantly reducing the number of floating-point calculations and of parameters, reducing the dependence on hardware, and providing a specific reference basis for deploying automated underground gangue sorting.
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Vanek, Bálint, Márton Farkas, and Szabolcs Rózsa. "Position and Attitude Determination in Urban Canyon with Tightly Coupled Sensor Fusion and a Prediction-Based GNSS Cycle Slip Detection Using Low-Cost Instruments." Sensors 23, no. 4 (February 14, 2023): 2141. http://dx.doi.org/10.3390/s23042141.
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We present a position and attitude estimation algorithm of moving platforms based on the tightly coupled sensor fusion of low-cost multi baseline GNSS, inertial, magnetic and barometric observations obtained by low-cost sensors and affordable dual-frequency GNSS receivers. The sensor fusion algorithm is realized by an Extended Kalman Filter and estimates the states including GNSS receiver inter-channel biases, integer ambiguities and non-GNSS receiver biases. Tightly coupled sensor fusion increases the reliability of the position and attitude solution in challenging environments such as urban canyons by utilizing the inertial observations in case of GNSS outage. Moreover, GNSS observations can be efficiently used to mitigate IMU sensor drifts. Standard GNSS cycle slips detection methods, such as the application of triple differences or linear combinations such as Melbourne–Wübbena combination and the phase ionospheric residual extended TurboEdit method. However, these techniques are not well suited for the localization in quickly changing environments such as urban canyons. We present a new method of tightly coupled sensor fusion supported by a prediction based cycle slip detection technique, applied to a GNSS setup using three antennas leading to multiple moving baselines on the platform. Thus, not only the GNSS signal properties but also the dynamics of the moving platform are considered in the cycle slip detection. The developed algorithm is tested in an open-sky validation measurement and two sets of measurement in an urban canyon area. The sensor fusion algorithm processes the data sets using the proposed prediction-based cycle slip method, the loss-of-lock indicator-based, and for comparison, the Melbourne–Wübbena and the TurboEdit cycle slip detection methods are also included. The obtained position and attitude estimation results are compared to the internal solution of raw data source GNSS receivers and to the observations of a high-accuracy GNSS/INS unit including a fiber optic gyro. The validation test confirms the proper cycle slip detection in an ideal environment. The more challenging urban canyon test results show the reliability and the accuracy of the proposed method. In the case of the second urban canyon test, the proposed method improved the integer ambiguity resolution success rate by 19% and these results show the lowest horizontal and vertical coordinate distortion in comparison of the linear combination and the loss-of-lock-based cycle slip methods.
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Ge, Yun, Jian F. Zhai, and Pei C. Su. "Traffic Flow Prediction Based on Multi-Spatiotemporal Attention Gated Graph Convolution Network." Journal of Advanced Transportation 2022 (September 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/2723101.
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Accurate prediction of traffic flow plays an important role in ensuring public traffic safety and solving traffic congestion. Because graph convolutional neural network (GCN) can perform effective feature calculation for unstructured data, doing research based on GCN model has become the main way for traffic flow prediction research. However, most of the existing research methods solving this problem are based on combining the graph convolutional neural network and recurrent neural network for traffic prediction. Such research routines have high computational cost and few attentions on impaction of different time and nodes. In order to improve the accuracy of traffic flow prediction, a gated attention graph convolution model based on multiple spatiotemporal channels was proposed in this paper. This model takes multiple time period data as input and extracts the features of each channel by superimposing multiple gated temporal and spatial attention modules. The final feature vector is obtained by means of weighted linear superposition. Experimental results on two sets of data show that the proposed method has good performance in precision and interpretability.
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Carbajal Henken, C. K., L. Doppler, R. Lindstrot, R. Preusker, and J. Fischer. "Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution." Atmospheric Measurement Techniques 8, no. 8 (August 24, 2015): 3419–31. http://dx.doi.org/10.5194/amt-8-3419-2015.
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Abstract. This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) algorithm, are based on independent measurements and different retrieval techniques. First, cloud-top temperature (CTT) is retrieved from Advanced Along Track Scanning Radiometer (AATSR) measurements in the thermal infrared. Second, cloud-top pressure (CTP) is retrieved from Medium Resolution Imaging Spectrometer (MERIS) measurements in the oxygen-A absorption band and a nearby window channel. Both CTT and CTP are converted to cloud-top height (CTH) using atmospheric profiles from a numerical weather prediction model. First, a sensitivity study using radiative transfer simulations in the near-infrared and thermal infrared was performed to demonstrate, in a quantitative manner, the larger impact of the assumed cloud vertical extinction profile, described in terms of shape and vertical extent, on MERIS than on AATSR top-of-atmosphere measurements. Consequently, cloud vertical extinction profiles will have a larger influence on the MERIS than on the AATSR cloud height retrievals for most cloud types. Second, the difference in retrieved CTH (ΔCTH) from AATSR and MERIS are related to cloud vertical extent (CVE), as observed by ground-based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. Similarly to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is stronger for single-layer clouds than for multi-layer clouds. Due to large variations of cloud vertical extinction profiles occurring in nature, a quantitative estimate of the cloud vertical extent is accompanied with large uncertainties. Yet, estimates of the CVE provide an additional parameter, next to CTH, that can be obtained from passive imager measurements and can be used to further describe cloud vertical distribution, thus contributing to the characterization of a cloudy scene. To further demonstrate the plausibility of the approach, an estimate of the CVE was applied to a case study. In light of the follow-up mission Sentinel-3 with AATSR and MERIS like instruments, Sea and Land Surface Temperature Radiometer (SLSTR) and (Ocean and Land Colour Instrument) OLCI, respectively, for which the FAME-C algorithm can be easily adapted, a more accurate estimate of the CVE can be expected. OLCI will have three channels in the oxygen-A absorption band, possibly providing enhanced information on cloud vertical distributions.
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Kouki, Rihab, Alexandre Boe, Thomas Vantroys, and Faouzi Bouani. "Autonomous Internet of Things predictive control application based on wireless networked multi-agent topology and embedded operating system." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 5 (August 29, 2019): 577–95. http://dx.doi.org/10.1177/0959651819870340.
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This article investigates the problem of data-driven cooperative tracking for a class of multi-agent linear systems under imperfect wireless communication. An autonomous Internet of Things predictive control application is designed to drive a robot with one wheel. The proposed methodology has been developed using Revolutionary Internet of Things Operating System running on STM32 and radio frequency communication shields over the User Datagram Protocol. To evaluate the performance of the predictive control algorithm, the User Datagram Protocol has been used due to the high number of packet losses in the communication channel. A robust analysis of Internet of Things technology among agents, combined with a network predictive control strategy against packet loss, limited bandwidth and attack links is carried out. The main feature of this methodology is that it is possible to achieve consensus monitoring and stability of closed-loop control systems. The efficiency of the proposed design approach is demonstrated by several experimental scenarios.
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Gersey, Christopher O., Thomas C. Willingham, and Issam Mudawar. "Design Parameters and Practical Considerations in the Two-Phase Forced-Convection Cooling of Multi-Chip Modules." Journal of Electronic Packaging 114, no. 3 (September 1, 1992): 280–89. http://dx.doi.org/10.1115/1.2905452.
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Forced-convection boiling was investigated with a dielectric coolant (FC-72) in order to address some of the practical issues related to the two-phase cooling of multi-chip modules. The module used in the present study featured a linear array of nine, 10 × 10 mm2, simulated microelectronic chips which were flush-mounted along a 20-mm wide side of a rectangular channel. Experiments were performed with a 5-mm channel gap (distance between the chip surface and the opposing channel wall) at eight orientations spaced 45 degrees apart. Two other channel gaps, 2 and 10 mm, were tested in the vertical up flow configuration. For all these configurations, the velocity and subcooling of the liquid were varied from 13 to 400 cm/s and 3 to 36°C, respectively. Changes in orientation did not affect single-phase or nucleate boiling characteristics, but did have a major impact on CHF. Upflow conditions were found to be the best configuration for the design of two-phase cooling modules because of its inherently stable flow and relatively high CHF values. The CHF value for the most upstream chip in vertical upflow agreed well with a previous correlation for an isolated chip. Combined with the relatively small spread in CHF values for all chips in the array, this correlation was found to be attractive for design purposes in predicting CHF for a multi-chip array. To achieve a given CHF value, it is shown how the strong CHF dependence on velocity rather than flow area allows for a reduction in the required flow rate with the 2-mm, as compared to the 5-mm gap, which also required a smaller flow rate than the 10-mm gap. This reduction inflow rate was significant only with subcooled conditions corresponding to high CHF values.
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Pang, Zhonghua, Tao Du, Changbing Zheng, and Chao Li. "Event-Triggered Cooperative Predictive Control for Networked Multi-Agent Systems with Random Delays and Packet Dropouts." Symmetry 14, no. 3 (March 7, 2022): 541. http://dx.doi.org/10.3390/sym14030541.
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This paper addresses the cooperative output tracking control problem for a class of leader-following linear heterogeneous networked multi-agent systems subject to random network delays and packet dropouts in the feedback and forward channels of each agent. A state observer is established at the plant side of each agent, and an event-triggering transmission mechanism is introduced to decide which state estimate is transmitted to the corresponding controller so as to save the network resources of the feedback channel. To further compensate for the negative effects of those random communication constraints and the event trigger, a cooperative predictive control scheme with proportional and integral actions is proposed. Then, a necessary and sufficient condition is derived for the stability of the resulting closed-loop system. Finally, simulation results are given to show the effectiveness of the proposed scheme.
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Clauser, C. F., W. Guttenfelder, T. Rafiq, and E. Schuster. "Linear ion-scale microstability analysis of high and low-collisionality NSTX discharges and NSTX-U projections." Physics of Plasmas 29, no. 10 (October 2022): 102303. http://dx.doi.org/10.1063/5.0102169.
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Linear gyrokinetic simulations were conducted to investigate ion-gyroradius-scale micro-instability predictions for high-beta NSTX discharges and NSTX-U projections that span over an order of magnitude variation in collisionality. A complex mix of microtearing modes and hybrid trapped electron modes/kinetic ballooning modes (TEM/KBM) is predicted for all experimental or projected conditions. Ion temperature gradient (ITG) instabilities are typically stable in the NSTX discharges investigated, consistent with the observed neoclassical ion thermal transport. ITG thresholds inferred from the simulations are typically much higher than the experimental NSTX gradients, as well as the projected gradients in the NSTX-U scenario, which assumed ion temperatures limited by neoclassical transport only. The analysis suggests ITG instabilities are unlikely to contribute significant anomalous thermal losses in high-beta, lower collisionality NSTX-U scenarios. On the other hand, the NSTX experimental profiles and NSTX-U projections are predicted to be very close to the predicted onset of unstable KBM at most radii investigated. The proximity of the various discharges to the KBM instability threshold implies it may play an important role in setting profile shapes and limiting global energy confinement. It remains to be understood and predicted how KBM contributes to multi-channel transport (thermal and particle transport, for both ions and electrons) in a way that is consistent with experimental inferences.
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POZRIKIDIS, C. "Gravity-driven creeping flow of two adjacent layers through a channel and down a plane wall." Journal of Fluid Mechanics 371 (September 25, 1998): 345–76. http://dx.doi.org/10.1017/s0022112098002213.
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We study the stability of the interface between (a) two adjacent viscous layers flowing due to gravity through an inclined or vertical channel that is confined between two parallel plane walls, and (b) two superimposed liquid films flowing down an inclined or vertical plane wall, in the limit of Stokes flow. In the case of channel flow, linear stability analysis predicts that, when the fluids are stably stratified, the flow is neutrally stable when the surface tension vanishes and the channel is vertical, and stable otherwise. This behaviour contrasts with that of the gravity-driven flow of two superimposed films flowing down an inclined plane, where an instability has been identified when the viscosity of the fluid next to the plane is less than that of the top fluid, even in the absence of fluid inertia. We investigate the nonlinear stages of the motion subject to finite-amplitude two-dimensional perturbations by numerical simulations based on boundary-integral methods. In both cases of channel and film flow, the mathematical formulation results in integral equations for the unknown interface and free-surface velocity. The properties of the integral equation for multi-film flow are investigated with reference to the feasibility of computing a solution by the method of successive substitutions, and a deflation strategy that allows an iterative procedure is developed. In the case of channel flow, the numerical simulations show that disturbances of sufficiently large amplitude may cause permanent deformation in which the interface folds or develops elongated fingers. The ratio of the viscosities and densities of the two fluids plays an important role in determining the morphology of the emerging interfacial patterns. Comparing the numerical results with the predictions of a model based on the lubrication approximation shows that the simplified approach can only describe a limited range of motions. In the case of film flow down an inclined plane, we develop a method for extracting the properties of the normal modes, including the ratio of the amplitudes of the free-surface and interfacial waves and their relative phase lag, from the results of a numerical simulation for small deformations. The numerical procedure employs an adaptation of Prony's method for fitting a signal described by a time series to a sum of complex exponentials; in the present case, the signal is identified with the cosine or sine Fourier coefficients of the interface and free-surface waves. Numerical simulations of the nonlinear motion confirm that the deformability of the free surface is necessary for the growth of small-amplitude perturbations, and show that the morphology of the interfacial patterns developing subject to finite-amplitude perturbations is qualitatively similar to that for channel flow.
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Becker, Keith, Jim Sprigg, and Alex Cosmas. "Estimating individual promotional campaign impacts through Bayesian inference." Journal of Consumer Marketing 31, no. 6/7 (November 4, 2014): 541–52. http://dx.doi.org/10.1108/jcm-06-2014-1006.
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Purpose – The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the impact of direct marketing promotions on purchase behavior. However, many modern marketing programs must drive multiple purchase objectives, requiring more precise arbitration between multiple offers and collection of more data with which to differentiate individuals. This often results in datasets that are highly dimensional, yet also sparse, straining the power of statistical methods to properly estimate the effect of promotional treatments. Design/methodology/approach – Improvements in computing power have enabled new techniques for predicting individual behavior. This work investigates a probabilistic machine-learned Bayesian approach to predict individual impacts driven by promotional campaign offers for a leading global travel and hospitality chain. Comparisons were made to a linear regression, representative of the current state of practice. Findings – The findings of this work focus on comparing a machine-learned Bayesian approach with linear regression (which is representative of the current state of practice among industry practitioners) in the analysis of a promotional campaign across three key areas: highly dimensional data, sparse data and likelihood matching. Research limitations/implications – Because the findings are based on a single campaign, future work includes generalizing results across multiple promotional campaigns. Also of interest for future work are comparisons of the technique developed here with other techniques from academia. Practical implications – Because the Bayesian approach allows estimation of the influence of the promotion for each hypothetical customer’s set of promotional attributes, even when no exact look-alikes exist in the control group, a number of possible applications exist. These include optimal campaign design (given the ability to estimate the promotional attributes that are likely to drive the greatest incremental spend in a hypothetical deployment) and operationalizing efficient audience selection given the model’s individualized estimates, reducing the risk of marketing overcommunication, which can prompt costly unsubscriptions. Originality/value – The original contribution is the application of machine-learning to Bayesian Belief Network construction in the context of analyzing a multi-channel promotional campaign’s impact on individual customers. This is of value to practitioners seeking alternatives for campaign analysis for applications in which more commonly used models are not well-suited, such as the three key areas that this paper highlights: highly dimensional data, sparse data and likelihood matching.
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NIKORA, VLADIMIR I., ALEXANDER N. SUKHODOLOV, and PAWEL M. ROWINSKI. "Statistical sand wave dynamics in one-directional water flows." Journal of Fluid Mechanics 351 (November 25, 1997): 17–39. http://dx.doi.org/10.1017/s0022112097006708.
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Moving sand waves and the overlying tubulent flow were measured on the Wilga River in Poland, and the Tirnava Mica and Buzau Rivers in Romania. Bottom elevations and flow velocities were measured at six points simultaneously by multi-channel measuring systems. From these data, the linear and two-dimensional sections of the three-dimensional correlation and structure functions and various projections of sand wave three-dimensional spectra were investigated.It was found that the longitudinal wavenumber spectra of the sand waves in the region of large wavenumbers followed Hino's −3 law (S(Kx) ∝K−3x) quite satisfactorily, confirming the theoretical predictions of Hino (1968) and Jain & Kennedy (1974). However, in contrast to Hino (1968), the sand wave frequency spectrum in the high-frequency region was approximated by a power function with the exponent −2, while in the lower-frequency region this exponent is close to −3.A dispersion relation for sand waves has been investigated from analysis of structure functions, frequency spectra and the cross-correlation functions method. For wavelengths less than 0.15–0.25 of the flow depth, their propagation velocity C is inversely proportional to the wavelength λ. When the wavelengths of spectral components are as large as 3–4 times the flow depth, no dispersion occurs. These results proved to be in good qualitative agreement with the theoretical dispersion relation derived from the potential-flow-based analytical models (Kennedy 1969; Jain & Kennedy 1974). We also present another, physically-based, explanation of this phenomenon, introducing two types of sand movement in the form of sand waves. The first type (I) is for the region of large wavenumbers (small wavelengths) and the second one (II) is for the region of small wavenumbers (large wavelengths). The small sand waves move due to the motion of individual sand particles (type I, C∝λ−1) while larger sand waves propagate as a result of the motion of smaller waves on their upstream slopes (type II, C∝λ0). Like the sand particles in the first type, these smaller waves redistribute sand from upstream slopes to downstream ones. Both types result in sand wave movement downstream but with a different propagation velocity.The main characteristics of turbulence, as well as the quantitative values characterizing the modulation of turbulence by sand waves, are also presented.
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Malone, Brendan P., Alex B. McBratney, and Budiman Minasny. "Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia." PeerJ 6 (April 16, 2018): e4659. http://dx.doi.org/10.7717/peerj.4659.
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Soil colour is often used as a general purpose indicator of internal soil drainage. In this study we developed a necessarily simple model of soil drainage which combines the tacit knowledge of the soil surveyor with observed matrix soil colour descriptions. From built up knowledge of the soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining soils generally follows the colour sequence of red → brown → yellow → grey → black soil matrix colours. For each soil profile, soil drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference soil colours of the soil drainage colour sequence. The estimation of drainage index at each profile incorporates the whole-profile descriptions of soil colour where necessary, and is weighted such that observation of soil colour at depth and/or dominantly observed horizons are given more preference than observations near the soil surface. The soil drainage index, by definition disregards surficial soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of soil drainage we digitally mapped the index across our study area. Spatial inference of the drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson’s correlation coefficients indicated the variables most strongly correlated with soil drainage were topographic wetness index (−0.34), mid-slope position (−0.29), multi-resolution valley bottom flatness index (−0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of soil drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 soil drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of soil colour description, these results are acceptable. Furthermore, the spatial distribution of soil drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique soil colour and soil drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged.
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Fang, Yin-Ying, Chi-Fang Chen, and Sheng-Ju Wu. "Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan." ANZIAM Journal 59 (July 25, 2019): C318—C357. http://dx.doi.org/10.21914/anziamj.v59i0.12655.
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Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets.
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Sofos, Filippos, and Theodoros E. Karakasidis. "Nanoscale slip length prediction with machine learning tools." Scientific Reports 11, no. 1 (June 15, 2021). http://dx.doi.org/10.1038/s41598-021-91885-x.
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AbstractThis work incorporates machine learning (ML) techniques, such as multivariate regression, the multi-layer perceptron, and random forest to predict the slip length at the nanoscale. Data points are collected both from our simulation data and data from the literature, and comprise Molecular Dynamics simulations of simple monoatomic, polar, and molecular liquids. Training and test points cover a wide range of input parameters which have been found to affect the slip length value, concerning dynamical and geometrical characteristics of the model, along with simulation parameters that constitute the simulation conditions. The aim of this work is to suggest an accurate and efficient procedure capable of reproducing physical properties, such as the slip length, acting parallel to simulation methods. Non-linear models, based on neural networks and decision trees, have been found to achieve better performance compared to linear regression methods. After the model is trained on representative simulation data, it is capable of accurately predicting the slip length values in regions between or in close proximity to the input data range, at the nanoscale. Results also reveal that, as channel dimensions increase, the slip length turns into a size-independent material property, affected mainly by wall roughness and wettability.
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Jin, Xin, Xin Liu, Jinyun Guo, and Yi Shen. "Analysis and prediction of polar motion using MSSA method." Earth, Planets and Space 73, no. 1 (July 21, 2021). http://dx.doi.org/10.1186/s40623-021-01477-2.
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AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.
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Yang, Liu, Hanxin Chen, Yao Ke, Menglong Li, Lang Huang, and Yuzhuo Miao. "Multi-source and multi-fault condition monitoring based on parallel factor analysis and sequential probability ratio test." EURASIP Journal on Advances in Signal Processing 2021, no. 1 (July 13, 2021). http://dx.doi.org/10.1186/s13634-021-00730-w.
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AbstractThe monitoring of mechanical equipment systems contains an increasing number of complex content, expanding from traditional time, and frequency information to three-dimensional data of the time, space, and frequency information, and even higher-dimensional data containing subjects, experimental conditions. For high-dimensional data analysis, traditional decomposition methods such as Hilbert transform, fast Fourier transformation, and Gabor transformation not only lose the integrity of the data, but also increase the amount of calculation and introduce a lot of redundant information. The phenomenon of feature coupling, aliasing, and redundancy between the mechanical multi-source data signals will cause the inaccuracy of the evaluation, diagnosis, and prediction of industrial production operation status. The analysis of the three-way tensor composed of channel, frequency, and time is called parallel factor analysis (PARAFAC). The properties between the parallel factor analysis results and the input signals are studied through simulation experiments. Parallel factor analysis is used to decompose the third-order tensor composed of channel-time-frequency after continuous wavelet transformation of vibration signal into channel, time, and frequency characteristics. Multi-scale parallel factor analysis successfully extracted non-linear multi-dimensional dynamic fault characteristics by generating the spatial, spectral, time-domain signal loading value and three-dimensional fault characteristic expression. In order to verify the effectiveness of the space, frequency, and time domain signal loading values of the fault characteristic factors generated by the centrifugal pump system after parallel factor analysis, the characteristic factors obtained after parallel factor analysis are used as the SPRT test sequence for identification and verification. The results indicate that the method proposed in this article improves the measurement accuracy and intelligence of mechanical fault detection.
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Degirmenci, Murside, Yilmaz Kemal Yuce, Matjaž Perc, and Yalcin Isler. "Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs." Frontiers in Human Neuroscience 17 (July 11, 2023). http://dx.doi.org/10.3389/fnhum.2023.1223307.
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In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.
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Hua, Zhongwei, and Min Guan. "Lightweight sandy vegetation object detection algorithm based on attention mechanism." Journal of Agricultural Engineering, November 21, 2022. http://dx.doi.org/10.4081/jae.2022.1471.
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To solve the object detection task in the harsh sandy environment, this paper proposes a lightweight sandy vegetation object detection algorithm based on attention mechanism. We reduce the number of model parameters by lightweight design of the anchor-free object detection algorithm model, thereby reducing the model inference time and memory cost. Specifically, the algorithm uses a lightweight backbone network to extract features, and uses linear interpolation in the neck network to achieve multi-scale. Model algorithm compression is performed by depthwise separable convolution in the head network. At the same time, the channel attention mechanism is added to the model to further optimize the algorithm. Experiments have proved the superiority of the algorithm, the mAP in the training effect is 76%, and the prediction time per frame is 0.0277 seconds. It realizes the efficiency and accuracy of the algorithm operation in the desert environment.
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Hou, Miaole, Wuchen Hao, Youqiang Dong, and Yuhang Ji. "A detection method for the ridge beast based on improved YOLOv3 algorithm." Heritage Science 11, no. 1 (August 8, 2023). http://dx.doi.org/10.1186/s40494-023-00995-4.
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AbstractThe ridge beast is a beast placed on the ridge of the roof of ancient Chinese buildings, not only has a decorative function, and has a strict hierarchical meaning, the number and form of the ridge beast placed on different levels of buildings are strictly limited. The detection technology of ridge beast decorative parts has important application value in the fields of fine 3D reconstruction of ancient buildings, historical dating and cultural and tourism services. Aiming at the problem of poor detection performance of traditional detection algorithms due to high texture similarity and poor discrimination of ridge beast, this paper proposed an improved YOLOv3 based detection algorithm for ridge beast decorative pieces. In terms of basic network improvement, local features are aggregated to the deep separable convolution internal embedding summation layer, and point convolution is used to connect the channel information of original features and aggregated features, so as to expand the receptive field and learn more diverse features. The residual structure of the feature extraction network was constructed by using the convolution, and the extraction effect of the model on the fine-grained features of the ridge beast was optimized, so that the detection accuracy was improved. In the prediction head improvement of the model, the original linear structure was reconstructed, and the extrusion and excitation modules were introduced to model the channel relationship of multi-scale feature map, which suppressed the response of interference signals and made the feature more directivity. The parallel 1 × 1 and 3 × 3 convolution are used to construct a multi-size convolution structure, which enhances the semantic information extraction ability of the model and further improves the detection effect. Experiments were conducted on the constructed ridge-beast dataset, and the results showed that the mAP of the improved algorithm can reach 86.48%, which is 3.05% higher than YOLO-v3, and the model parameters are reduced by 70%, which has a better detection performance and can provide a reference for the automated detection of ancient building components.
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Mitra, Indrasis, and Indranil Ghosh. "Analytical Study On Intricacies of Axial Conduction in Microchannel Heat Sinks." Journal of Heat Transfer, June 9, 2022. http://dx.doi.org/10.1115/1.4054772.
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Abstract Microchannel heat sinks are potential devices capable of removing heat flux from high power density miniaturized electronic components. While the large surface-area-to-volume ratio and high heat transfer coefficient are the key features rendering benefits, the small flow rate, short channel lengths alongside high solid cross-section to fluid free flow area, make them susceptible to intense axial conduction loss. The conventional models for macro-devices based on the one-dimensional energy equation are often inappropriate in the micro domain. A novel multi-dimensional analytical model (capable of capturing axial heat transfer in microchannel heat sinks) has been used to study the thermal performance over a varied range of geometric and flow parameters. The effect of axial conduction has been seen in the solid-fluid temperature profiles, interfacial flux distribution, local Nusselt number variation and the average amount of heat transferred axially. The results indicate a skewed flux distribution at the fluid-solid interface leading to non-linear temperature variation and non-uniform local Nusselt number, when axial conduction is dominant. Moreover, it has been shown that non-linearity in the fluid temperature introduces significant errors in experimental data reduction, leading to apparently very low Nusselt number estimation. Moreover, this erroneous data interpretation is also linked to the prediction of Reynolds number dependency of the average Nusselt number in the laminar flow regime.
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Amiriparian, Shahin, Maurice Gerczuk, Sandra Ottl, Lukas Stappen, Alice Baird, Lukas Koebe, and Björn Schuller. "Towards cross-modal pre-training and learning tempo-spatial characteristics for audio recognition with convolutional and recurrent neural networks." EURASIP Journal on Audio, Speech, and Music Processing 2020, no. 1 (December 2020). http://dx.doi.org/10.1186/s13636-020-00186-0.
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AbstractIn this paper, we investigate the performance of two deep learning paradigms for the audio-based tasks of acoustic scene, environmental sound and domestic activity classification. In particular, a convolutional recurrent neural network (CRNN) and pre-trained convolutional neural networks (CNNs) are utilised. The CRNN is directly trained on Mel-spectrograms of the audio samples. For the pre-trained CNNs, the activations of one of the top layers of various architectures are extracted as feature vectors and used for training a linear support vector machine (SVM).Moreover, the predictions of the two models—the class probabilities predicted by the CRNN and the decision function of the SVM—are combined in a decision-level fusion to achieve the final prediction. For the pre-trained CNN networks we use as feature extractors, we further evaluate the effects of a range of configuration options, including the choice of the pre-training corpus. The system is evaluated on the acoustic scene classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2017) workshop, ESC-50 and the multi-channel acoustic recordings from DCASE 2018, task 5. We have refrained from additional data augmentation as our primary goal is to analyse the general performance of the proposed system on different datasets. We show that using our system, it is possible to achieve competitive performance on all datasets and demonstrate the complementarity of CRNNs and ImageNet pre-trained CNNs for acoustic classification tasks. We further find that in some cases, CNNs pre-trained on ImageNet can serve as more powerful feature extractors than AudioSet models. Finally, ImageNet pre-training is complimentary to more domain-specific knowledge, either in the form of the convolutional recurrent neural network (CRNN) trained directly on the target data or the AudioSet pre-trained models. In this regard, our findings indicate possible benefits of applying cross-modal pre-training of large CNNs to acoustic analysis tasks.