Journal articles: 'Channel prediction'

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Gao, Jianzhao, Hong Wei, Alberto Cano, and Lukasz Kurgan. "PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types." Biomolecules 10, no. 6 (June 7, 2020): 876. http://dx.doi.org/10.3390/biom10060876.

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Computational prediction of ion channels facilitates the identification of putative ion channels from protein sequences. Several predictors of ion channels and their types were developed in the last quindecennial. While they offer reasonably accurate predictions, they also suffer a few shortcomings including lack of availability, parallel prediction mode, single-label prediction (inability to predict multiple channel subtypes), and incomplete scope (inability to predict subtypes of the voltage-gated channels). We developed a first-of-its-kind PSIONplusm method that performs sequential multi-label prediction of ion channels and their subtypes for both voltage-gated and ligand-gated channels. PSIONplusm sequentially combines the outputs produced by three support vector machine-based models from the PSIONplus predictor and is available as a webserver. Empirical tests show that PSIONplusm outperforms current methods for the multi-label prediction of the ion channel subtypes. This includes the existing single-label methods that are available to the users, a naïve multi-label predictor that combines results produced by multiple single-label methods, and methods that make predictions based on sequence alignment and domain annotations. We also found that the current methods (including PSIONplusm) fail to accurately predict a few of the least frequently occurring ion channel subtypes. Thus, new predictors should be developed when a larger quantity of annotated ion channels will be available to train predictive models.

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Yue, Yingbo, Fuchun Chen, and Guilin Chen. "Statistical and Comparative Analysis of Multi-Channel Infrared Anomalies before Earthquakes in China and the Surrounding Area." Applied Sciences 12, no. 16 (August 9, 2022): 7958. http://dx.doi.org/10.3390/app12167958.

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Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction.

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Gao, Jianzhao, Zhen Miao, Zhaopeng Zhang, Hong Wei, and Lukasz Kurgan. "Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment." Current Drug Targets 20, no. 5 (March 5, 2019): 579–92. http://dx.doi.org/10.2174/1389450119666181022153942.

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Background: Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated. Objective: We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors. Results: While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types. Conclusion: Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.

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Klingaa, Christopher Gottlieb, Sankhya Mohanty, and Jesper Henri Hattel. "Realistic design of laser powder bed fusion channels." Rapid Prototyping Journal 26, no. 10 (October 15, 2020): 1827–36. http://dx.doi.org/10.1108/rpj-01-2020-0010.

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Purpose Conformal cooling channels in additively manufactured molds are superior over conventional channels in terms of cooling control, part warpage and lead time. The heat transfer ability of cooling channels is determined by their geometry and surface roughness. Laser powder bed fusion manufactured channels have an inherent process-induced dross formation that may significantly alter the actual shape of nominal channels. Therefore, it is crucial to be able to predict the expected surface roughness and changes in the geometry of metal additively manufactured conformal cooling channels. The purpose of this paper is to present a new methodology for predicting the realistic design of laser powder bed fusion channels. Design/methodology/approach This study proposes a methodology for making nominal channel design more realistic by the implementation of roughness prediction models. The models are used for altering the nominal shape of a channel to its predicted shape by point cloud analysis and manipulation. Findings A straight channel is investigated as a simple case study and validated against X-ray computed tomography measurements. The modified channel geometry is reconstructed and meshed, resulting in a predicted, more realistic version of the nominal geometry. The methodology is successfully tested on a torus shape and a simple conformal cooling channel design. Finally, the methodology is validated through a cooling test experiment and comparison with simulations. Practical implications Accurate prediction of channel surface roughness and geometry would lead toward more accurate modeling of cooling performance. Originality/value A robust start to finish method for realistic geometrical prediction of metal additive manufacturing cooling channels has yet to be proposed. The current study seeks to fill the gap.

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Ra, Jee S., Tianning Li, and Yan Li. "A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction." Sensors 21, no. 23 (November 29, 2021): 7972. http://dx.doi.org/10.3390/s21237972.

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The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.

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M. Al-Sammna, Ahmed, Marwan Hadri Azmi, and Tharek Abd Rahman. "Time-Varying Ultra-Wideband Channel Modeling and Prediction." Symmetry 10, no. 11 (November 12, 2018): 631. http://dx.doi.org/10.3390/sym10110631.

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This paper considers the channel modeling and prediction for ultra-wideband (UWB) channels. The sparse property of UWB channels is exploited, and an efficient prediction framework is developed by introducing two simplified UWB channel impulse response (CIR) models, namely, the windowing-based on window delay (WB-WD) and the windowing-based on bin delay (WB-BD). By adopting our proposed UWB windowing-based CIR models, the recursive least square (RLS) algorithm is used to predict the channel coefficients. By using real CIR coefficients generated from measurement campaign data conducted in outdoor environments, the modeling and prediction performance results and the statistical properties of the root mean square (RMS) delay spread values are presented. Our proposed framework improves the prediction performances with lower computational complexity compared with the performance of the recommended ITU-R UWB-CIR model. It is shown that our proposed framework can achieved 15% lower prediction error with a complexity reduction by a factor of 12.

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Hagebölling, F., and U. Zölzer. "A multi channel coupling based approach for the prediction of the channel capacity of MIMO-systems." Advances in Radio Science 9 (July 29, 2011): 153–58. http://dx.doi.org/10.5194/ars-9-153-2011.

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Abstract. When installing Multiple Input Multiple Output (MIMO)-systems, the antenna positioning has a major influence upon the achievable transmission quality. To determine those antenna positions, which maximize the transmission quality, in adequate time, a computer based prediction of the channel capacity is imperative. In this paper, we will show that Ray Tracing, which is a very popular prediction method and well suited for the prediction of transfer functions or power delay profiles, produces unacceptable errors when predicting the channel capacity of MIMO-systems. Furthermore we identify the source of the prediction errors and present a new algorithm, based on an approach known as Multi Channel Coupling (MCC), which avoids this error source. Finally a comparison of the prediction results of our algorithm with prediction results gained with an Image Ray Tracer as well as with measured results is used to show the formidable increasement of prediction accuracy which can be gained by using our algorithm.

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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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Zhao, Zijian, and Pengyuan Zou. "Attention-based Dual-channel Deep Neural Network For Aero-engine RUL Prediction Under Time-varying Operating Conditions." Journal of Physics: Conference Series 2386, no. 1 (December 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2386/1/012027.

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Abstract For complex systems such as aerospace, remaining useful life (RUL) prediction is a general technique that provides information for decision-making in predictive maintenance. In the industrial field, RUL prediction under time-varying operating conditions is a challenging task. In this paper, an attention-based dual-channel deep neural network is proposed to fuse the time-varying operating conditions, with both prediction channels using long short-term memory (LSTM) neural networks. First, the features are extracted by a one-dimensional convolutional neural network (CNN). The operating conditions and sensor data are put into the dual-channel LSTM neural networks separately for prediction. The obtained results are combined with the attention mechanism to assign weights and finally put into the fully connected network for linear mapping to get the final RUL prediction results. This study is based on the N-CMAPSS dataset published by NASA. Compared with traditional methods, this method demonstrates its superiority and effectiveness.

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Song, Yamin, Xia Lei, Zhaofu Kong, and Binhong Dong. "Antenna calibration using channel prediction for time-varying channels." Journal of Modern Transportation 20, no. 4 (December 2012): 213–19. http://dx.doi.org/10.1007/bf03325801.

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Saghebian, Seyed Mahdi. "Predicting the relative energy dissipation of hydraulic jump in rough and smooth bed compound channels using SVM." Water Supply 19, no. 4 (September 27, 2018): 1110–19. http://dx.doi.org/10.2166/ws.2018.162.

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Abstract Channels with different shapes and bed conditions are used as useful appurtenances to dissipate the extra energy of a hydraulic jump. Accurate prediction of hydraulic jump energy dissipation is important in design of hydraulic structures. In the current study, hydraulic jump energy dissipation was assessed in channels with different shapes and bed conditions (i.e. smooth and rough beds) using the support vector machine (SVM) as an intelligence approach. Five series of experimental datasets were applied to develop the models. The results showed that the SVM model is successful in estimating the relative energy dissipation. For the smooth bed, it was observed that the sloping channel models with steps performed more successfully than rectangular and trapezoidal channels and the step height is an effective variable in the estimation process. For the rough bed, the trapezoidal channel models were more accurate than the rectangular channel. It was found that rough element geometry is effective in estimation of the energy dissipation. The result showed that the models of rough channels led to better predictions. The sensitivity analysis results revealed that Froude number had the more dominant role in the modeling. Comparison among SVM and two other intelligence approaches showed that SVM is more successful in the prediction process.

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Li, Yue, Yan Yi, Dong Liu, Li Li, Zhu Li, and Houqiang Li. "Neural-Network-Based Cross-Channel Intra Prediction." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3 (July 22, 2021): 1–23. http://dx.doi.org/10.1145/3434250.

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To reduce the redundancy among different color channels, e.g., YUV, previous methods usually adopt a linear model that tends to be oversimple for complex image content. We propose a neural-network-based method for cross-channel prediction in intra frame coding. The proposed network utilizes twofold cues, i.e., the neighboring reconstructed samples with all channels, and the co-located reconstructed samples with partial channels. Specifically, for YUV video coding, the neighboring samples with YUV are processed by several fully connected layers; the co-located samples with Y are processed by convolutional layers; and the proposed network fuses the twofold cues. We observe that the integration of twofold information is crucial to the performance of intra prediction of the chroma components. We have designed the network architecture to achieve a good balance between compression performance and computational efficiency. Moreover, we propose a transform domain loss for the training of the network. The transform domain loss helps obtain more compact representations of residues in the transform domain, leading to higher compression efficiency. The proposed method is plugged into HEVC and VVC test models to evaluate its effectiveness. Experimental results show that our method provides more accurate cross-channel intra prediction compared with previous methods. On top of HEVC, our method achieves on average 1.3%, 5.4%, and 3.8% BD-rate reductions for Y, Cb, and Cr on common test sequences, and on average 3.8%, 11.3%, and 9.0% BD-rate reductions for Y, Cb, and Cr on ultra-high-definition test sequences. On top of VVC, our method achieves on average 0.5%, 1.7%, and 1.3% BD-rate reductions for Y, Cb, and Cr on common test sequences.

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Briggs, Michael J., Paul J. Kopp, Vladimir K. Ankudinov, and Andrew L. Silver. "Comparison of Measured Ship Squat with Numerical and Empirical Methods." Journal of Ship Research 57, no. 02 (June 1, 2013): 73–85. http://dx.doi.org/10.5957/jsr.2013.57.2.73.

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The Beck, Newman and Tuck (BNT) numerical predictions are used in the Coastal and Hydraulics Laboratory (CHL) Channel Analysis and Design Evaluation Tool (CADET) model for predicting underkeel clearance (UKC) resulting from ship motions and squat. The Ankudinov empirical squat prediction formula has been used in the CHL ship simulator and was recently updated. The World Association for Waterborne Transport Infrastructure (formerly The Permanent International Association of Navigation Congresses, PIANC) has recommended several empirical and physics-based formulas for the prediction of ship squat. Some of the most widely used formulas include those of Barrass, Eryuzlu, Huuska, ICORELS, Romisch, Tuck, and Yoshimura. The purpose of this article is to compare BNT, Ankudinov, and PIANC predictions with measured DGPS squat data from the Panama Canal for four ships. These comparisons demonstrate that the BNT, Ankudinov, and PIANC predictions fall within the range of squat measurements and can be used with confidence in deep draft channel design.

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Pakari, Ali, and Saud Ghani. "Regression Models for Performance Prediction of Internally-Cooled Liquid Desiccant Dehumidifiers." Energies 15, no. 5 (February 26, 2022): 1758. http://dx.doi.org/10.3390/en15051758.

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In this study, using response surface methodology and central composite design, regression models were developed relating 12 input factors to the supply air outlet humidity ratio and temperature of 4-fluid internally-cooled liquid desiccant dehumidifiers. The selected factors are supply air inlet temperature, supply air inlet humidity ratio, exhaust air inlet temperature, exhaust air inlet humidity ratio, liquid desiccant inlet temperature, liquid desiccant concentration, liquid desiccant flow rate, supply air mass flow rate, the ratio of exhaust to supply air mass flow rate, the thickness of the channel, the channel length, and the channel width of the dehumidifier. The designed experiments were performed using a numerical two-dimensional heat and mass transfer model of the liquid desiccant dehumidifier. The numerical model predicted the measured values of the supply air outlet humidity ratio within 6.7%. The regression model’s predictions of the supply air outlet humidity ratio matched the numerical model’s predictions and measured values within 4.5% and 7.9%, respectively. The results showed that the input factors with the most significant effect on the dehumidifying process in order of significance from high to low are as follows: supply air inlet humidity ratio, liquid desiccant concertation, length of channels, and width of channels. The developed regression models provide a straightforward means for performance prediction and optimization of internally-cooled liquid desiccant dehumidifiers.

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Wu, Shanchan, Tamer Elsayed, William Rand, and Louiqa Raschid. "Predicting Author Blog Channels with High Value Future Posts for Monitoring." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1261–66. http://dx.doi.org/10.1609/aaai.v25i1.8093.

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The phenomenal growth of social media, both in scale and importance, has created a unique opportunity to track information diffusion and the spread of influence, but can also make efficient tracking difficult. Given data streams representing blog posts on multiple blog channels and a focal query post on some topic of interest, our objective is to predict which of those channels are most likely to contain a future post that is relevant, or similar, to the focal query post. We denote this task as the future author prediction problem (FAPP). This problem has applications in information diffusion for brand monitoring and blog channel personalization and recommendation. We develop prediction methods inspired by (naive) information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem. We evaluate our methods on an extensive social media dataset; despite the difficulty of the task, all methods perform reasonably well. Results show that ranking SVM prediction can exploit blog channel and diffusion characteristics to improve prediction accuracy. Moreover, it is surprisingly good for prediction in emerging topics and identifying inconsistent authors.

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Saghebian, Seyed Mahdi, Daniel Dragomir-Stanciu, and Roghayeh Ghasempour. "Assessing the Capability of KELM Meta-Model Approach in Predicting the Energy Dissipation in Different Shapes Channels." Proceedings 63, no. 1 (December 23, 2020): 45. http://dx.doi.org/10.3390/proceedings2020063045.

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For transition of a supercritical flow into a subcritical flow in an open channel, a hydraulic jump phenomenon is used. Different shaped channels are used as useful tools in the extra energy dissipation of the hydraulic jump. Accurate prediction of relative energy dissipation is important in designing hydraulic structures. The aim of this paper is to assess the capability of a Kernel extreme Learning Machine (KELM) meta-model approach in predicting the energy dissipation in different shaped channels (i.e., rectangular and trapezoidal channels). Different experimental data series were used to develop the models. The obtained results approved the capability of the KELM model in predicting the energy dissipation. Results showed that the rectangular channel led to better outcomes. Based on the results obtained for the rectangular and trapezoidal channels, the combination of Fr1, (y2-y1)/y1, and W/Z parameters performed more successfully. Also, comparison between KELM and the Artificial Neural Networks (ANN) approach showed that KELM is more successful in the predicting process.

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Roushangar, Kiyoumars, and Roghayeh Ghasempour. "Explicit prediction of expanding channels hydraulic jump characteristics using gene expression programming approach." Hydrology Research 49, no. 3 (August 11, 2017): 815–30. http://dx.doi.org/10.2166/nh.2017.262.

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Abstract Hydraulic jump is a useful means of dissipating excess energy of a supercritical flow so that objectionable scour downstream is minimized. The present study applies gene expression programming (GEP) to estimate hydraulic jump characteristics in sudden expanding channels. Three types of expanding channels were considered: channels without appurtenances, with a central sill, and with a negative step. 1,000 experimental data were considered as input data to develop models. The results proved the capability of GEP in predicting hydraulic jump characteristics in expanding channels. It was found that the developed models for channel with a central sill performed better than other channels. In the jump length prediction, the model with input parameters Fr1 and (y2—y1)/y1, and in the sequent depth ratio and relative energy dissipation prediction the model with input parameters Fr1 and y1/B led to more accurate outcomes (Fr1, y1, y2, and B are Froude number, sequent depth of upstream and downstream, and expansion ratio, respectively). Sensitivity analysis showed that Fr1 had the key role in modeling. The GEP models were compared with existing empirical equations and it was found that the GEP models yielded better results. It was also observed that channel and appurtenances geometry affected the modeling.

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Schaubach, K. R., and N. J. Davis. "Microcellular radio-channel propagation prediction." IEEE Antennas and Propagation Magazine 36, no. 4 (August 1994): 25–34. http://dx.doi.org/10.1109/74.317764.

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Potter, Chris, Ganesh K. Venayagamoorthy, and Kurt Kosbar. "RNN based MIMO channel prediction." Signal Processing 90, no. 2 (February 2010): 440–50. http://dx.doi.org/10.1016/j.sigpro.2009.07.013.

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Gao, Hao, Jian Lan, and Lin Hua. "Deformation Prediction of the Bipolar Plate Stamping." Advanced Materials Research 971-973 (June 2014): 270–74. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.270.

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Bipolar plate is the key component of proton exchange membrane (PEM) fuel cell and represents a significant part of the overall cost and the total weight in a fuel cell stack. Many research have been done on the manufacturing methods of bipolar plate, among which stamping is very popular. With the increasing of the channel number and complexity, its dimensional error caused by sprinkback will change a lot, even under the same forming process. And the risk of crack is also different. These all impact the quality of bipolar plate. In order to predict deformation of channels and the plate’s quality, the displacement along X-axis, the strain and stress state, and the displacement along Z-axis are measured. The results show that 1) the risk of crack increases with the increasing of channel number; 2) the springbacks increase with the increasing of channel number; 3) the most dangerous point locates on the right internal fillet of the plate’s last section.

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Ranatunga, Kishani M., Charlotte Adcock, Ian D. Kerr, Graham R. Smith, and Mark S. P. Sansom. "Ion channels of biological membranes: prediction of single channel conductance." Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta) 101, no. 1-3 (February 15, 1999): 97–102. http://dx.doi.org/10.1007/s002140050414.

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Akhtman, J., and L. Hanzo. "Channel Impulse Response Tap Prediction for Time-Varying Wireless Channels." IEEE Transactions on Vehicular Technology 56, no. 5 (September 2007): 2767–69. http://dx.doi.org/10.1109/tvt.2007.900395.

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Basicevic, Ilija, Dragan Kukolj, Stanislav Ocovaj, Gordana Cmiljanovic, and Nemanja Fimic. "A Fast Channel Change Technique Based on Channel Prediction." IEEE Transactions on Consumer Electronics 64, no. 4 (November 2018): 418–23. http://dx.doi.org/10.1109/tce.2018.2875271.

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Lin, Hao, and Wei Chen. "Briefing in Application of Machine Learning Methods in Ion Channel Prediction." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/945927.

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In cells, ion channels are one of the most important classes of membrane proteins which allow inorganic ions to move across the membrane. A wide range of biological processes are involved and regulated by the opening and closing of ion channels. Ion channels can be classified into numerous classes and different types of ion channels exhibit different functions. Thus, the correct identification of ion channels and their types using computational methods will provide in-depth insights into their function in various biological processes. In this review, we will briefly introduce and discuss the recent progress in ion channel prediction using machine learning methods.

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Lucas, Evan, and Zhaohui Wang. "Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses." Applied Sciences 12, no. 3 (January 20, 2022): 1086. http://dx.doi.org/10.3390/app12031086.

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Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the dataset. The universality of the learned features is also demonstrated by strong prediction performance when transferring from a more complex underwater acoustic channel to a simpler one.

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Li, Ting Ting, Xing Xing Zhang, Shi Zhong Ma, and Zhao Wang. "The Application of Peak Number Attribute in the Prediction of River Sand." Advanced Materials Research 838-841 (November 2013): 1591–94. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1591.

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Its a commonly used method to predict layer by using seismic attributes, especially for some of the less well control channel sand bodies whose role is more important. Putaohua reservoir in Gaotaizi oilfield mainly develop shallow water delta front subaqueous distributary channel sand bodies which has narrow rivers and thin sand bodies, meanwhile, the existing well density is difficult to control the trend and boundary of the channel. By using seismic forward modeling analysis techniques, this paper researched the differences of seismic reflection characteristics among different geological model of channel sand bodies, then , further pointed out the methods of channel sand prediction by using the peak number attribute and analyzed the predictive effect. The results show that this method can effectively improve the prediction accuracy of thin interbedded reservoir.

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Wang, Hai Yong, Chang Qing Huang, and Hua Deng. "Prediction on Transverse Thickness of Hot Rolling Aluminum Strip Based on BP Neural Network." Applied Mechanics and Materials 157-158 (February 2012): 78–83. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.78.

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For aluminum hot rolling process with multi-variables, strongly coupling, nonlinear, it is difficult to establish accurate mathematical model to solve transverse thickness distribution. A method based on BP neural network with hot rolling aluminum transverse thickness prediction was proposed; According to multi-channel measurement characteristics of IMS transverse thickness collection system, the BP neural network prediction model for single channel was established; predicting every channel thickness by BP neural network, transverse thickness distribution of aluminum strip was obtained. Simulation results compared with the measured datas, the relative error is less than 2.5%. The results indicate that the BP neural network is suitable for transverse thickness prediction of hot rolling aluminum strip.

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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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Ouyang, Hongxiang, Zhengkun Qin, and Juan Li. "Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin." Atmosphere 12, no. 10 (September 27, 2021): 1253. http://dx.doi.org/10.3390/atmos12101253.

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Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

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Hong, Il, Joongu Kang, Hongkoo Yeo, and Yonguk Ryu. "Channel Response Prediction for Abandoned Channel Restoration and Applicability Analysis." Engineering 03, no. 05 (2011): 461–69. http://dx.doi.org/10.4236/eng.2011.35053.

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Liu, Hongjie, Tianhao Li, Lingxiu Chen, Sha Zhan, Meilan Pan, Zhiguo Ma, Chenghua Li, and Zhe Zhang. "To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory." Evidence-Based Complementary and Alternative Medicine 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/7273940.

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Aims. To establish a logistic regression (LR) prediction model for hepatotoxicity of Chinese herbal medicines (HMs) based on traditional Chinese medicine (TCM) theory and to provide a statistical basis for predicting hepatotoxicity of HMs.Methods. The correlations of hepatotoxic and nonhepatotoxic Chinese HMs with four properties, five flavors, and channel tropism were analyzed with chi-square test for two-way unordered categorical data. LR prediction model was established and the accuracy of the prediction by this model was evaluated.Results. The hepatotoxic and nonhepatotoxic Chinese HMs were related with four properties (p<0.05), and the coefficient was 0.178 (p<0.05); also they were related with five flavors (p<0.05), and the coefficient was 0.145 (p<0.05); they were not related with channel tropism (p>0.05). There were totally 12 variables from four properties and five flavors for the LR. Four variables, warm and neutral of the four properties and pungent and salty of five flavors, were selected to establish the LR prediction model, with the cutoff value being 0.204.Conclusions. Warm and neutral of the four properties and pungent and salty of five flavors were the variables to affect the hepatotoxicity. Based on such results, the established LR prediction model had some predictive power for hepatotoxicity of Chinese HMs.

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Yan, Aixia, Zhi Wang, Jiaxuan Li, and Meng Meng. "Human Oral Bioavailability Prediction of Four Kinds of Drugs." International Journal of Computational Models and Algorithms in Medicine 3, no. 4 (October 2012): 29–42. http://dx.doi.org/10.4018/ijcmam.2012100104.

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In the development of drugs intended for oral use, good drug absorption and appropriate drug delivery are very important. Now the predictions for drug absorption and oral bioavailability follow similar approach: calculate molecular descriptors for molecules and build the prediction models. This approach works well for the prediction of compounds which cross a cell membrane from a region of high concentration to one of low concentration, but it does not work very well for the prediction of oral bioavailability, which represents the percentage of an oral dose which is able to produce a pharmacological activity. The models for bioavailability had limited predictability because there are a variety of pharmacokinetic factors influencing human oral bioavailability. Recent study has shown that good quantitative relationship could be obtained for subsets of drugs, such as those that have similar structure or the same pharmacological activity, or those that exhibit similar absorption and metabolism mechanisms. In this work, using MLR (Multiple Linear Regression) and SVM (Support Vector Machine), quantitative bioavailability prediction models were built for four kinds of drugs, which are Angiotensin Converting Enzyme Inhibitors or Angiotensin II Receptor Antagonists, Calcium Channel Blockers, Sodium and Potassium Channels Blockers and Quinolone Antimicrobial Agents. Explorations into subsets of compounds were performed and reliable prediction models were built for these four kinds of drugs. This work represents an exploration in predicting human oral bioavailability and could be used in other dataset of compounds with the same pharmacological activity.

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Ahmed, Siddig E., and Mohammed B. Saad. "Prediction of Natural Channel Hydraulic Roughness." Journal of Irrigation and Drainage Engineering 118, no. 4 (July 1992): 632–39. http://dx.doi.org/10.1061/(asce)0733-9437(1992)118:4(632).

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Jiang, Wei, and Hans Dieter Schotten. "Deep Learning for Fading Channel Prediction." IEEE Open Journal of the Communications Society 1 (2020): 320–32. http://dx.doi.org/10.1109/ojcoms.2020.2982513.

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ALLEN, P. M., J. ARNOLD, and E. JAKUBOWSKI. "Prediction of Stream Channel Erosion Potential." Environmental & Engineering Geoscience V, no. 3 (September 1, 1999): 339–51. http://dx.doi.org/10.2113/gseegeosci.v.3.339.

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Carter, Alison A., and Robert E. Oswald. "Linear prediction and single-channel recording." Journal of Neuroscience Methods 60, no. 1-2 (August 1995): 69–78. http://dx.doi.org/10.1016/0165-0270(94)00221-2.

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Dalzell, William G., and Colin F. N. Cowan. "Blind channel-shortening for multipath channels using a recursive prediction-error filter with additional prediction delay." IET Communications 7, no. 16 (November 5, 2013): 1844–51. http://dx.doi.org/10.1049/iet-com.2012.0583.

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More, Kiran P., and Rajendrakumar A. Patil. "Transmission State Prediction from MAC in Cognitive Radio via Optimized Deep Learning Architecture." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 09 (May 29, 2021): 2152012. http://dx.doi.org/10.1142/s0218001421520121.

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Cognitive Radio (CR) is the hottest network paradigm, which permits the Secondary Users (SUs) like wireless devices/users for intelligent accessing of unallocated radio spectrum. Such accessing happens by enabling interference-free transmission of Primary Users (PUs), who are allotted with some deserved radio spectrum portions. This radio communication paradigm has effective usage in vehicular networks, where communication should be established from vehicles to static stations (vehicle-to-infrastructure) or within vehicles (vehicle-to-vehicle), without allotting dedicated frequencies. Nevertheless, the major issue in designing CR is that it must be built to aid in efficient transmitting and sensing of data through the available radio spectrum channels. This paper proposes a Model Predictive Control (MPC)-based prediction model, via a Deep Learning approach. Here, a Deep Belief Network (DBN) allows in predicting the PU transmission state as idle or busy. Moreover, this paper comes out with a new optimization concept that achieves more accurate and precise prediction. The weight of DBN is optimally selected to pave way for effective performance. Further, a new hybrid algorithm named as Cuckoo Search-Grasshopper Optimization Algorithm (CS-GOA) is proposed. The performance of the proposed model is compared over the other conventional models, in terms of channel utilization and back off, and proved for supremacy. The throughput of the proposed model, even at 50 SUs is better, when compared to other methods. CS-GOA achieved better channel utilization and backoff rate, as compared to ProMAC and NN, when the numbers of SUs and PUs in the architecture are varied with time.

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Lv, Changwei, Shujuan Hou, and Wenbo Mei. "Adaptive Prediction of Channels with Sparse Features in OFDM Systems." International Journal of Antennas and Propagation 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/649602.

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A time domain channel prediction method exploiting features of sparse channel is proposed for orthogonal frequency division multiplexing (OFDM) systems. The proposed predictor operates in the time domain on each channel tap and separates the negligible taps from significant channel taps before performing prediction. We also compare the proposed prediction method with the classical frequency domain method realized at each OFDM subcarrier and demonstrate that our method increases the prediction accuracy and reduces the computational complexity. Simulations on the physical channel model verify the performance of the proposed method.

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Adegoke, James A., and Mutiu A. Fakunle. "Channel Flow and Flood Estimate." Annals of West University of Timisoara - Physics 59, no. 1 (December 1, 2016): 30–48. http://dx.doi.org/10.1515/awutp-2016-0005.

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AbstractThe movement of water on the land surface, within channels and through the soil is dependent on some hydrological factors. For surface flow, the velocity of flow increases with the bottom gradient of the channel and the flow depth, but when roughness increases, it decreases. For a given flow depth, the velocity decreases as the channel height increases. The construction of sub-surface drainage helps to remove excess soil water that can cause flood. To model overland flow, a kinematic-wave approach is applied so that flood prediction could be made.

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Yun, Jung Hoon, and Ji Hwan Jeong. "A Review of Prediction Methods for Two-Phase Pressure Loss in Mini/Micro-Channels." International Journal of Air-Conditioning and Refrigeration 24, no. 01 (March 2016): 1630002. http://dx.doi.org/10.1142/s2010132516300020.

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Previous methods and correlations for predicting two-phase frictional pressure loss in mini/micro-channels are reviewed and compared. The empirical correlations are classified into four groups of modeling approaches: Homogeneous equilibrium models (HEMs), separated flow models (SFMs), direct empirical correlations, and flow pattern specific correlations. In order to examine the characteristics of the predictive methods for two-phase pressure loss in mini-channels and to assess the accuracy of the previous models and correlations, extensive experimental data and correlations that are available in the open literature are collected. The 1175 and 1304 experimental data for the two-phase pressure drop for condensing and boiling flows, respectively, are gathered from 15 papers and reports. The results present that the size of the channel significantly influences the pressure drop. The comparison demonstrates that Cicchitti et al.’s two-phase viscosity model is recommended for predicting two-phase pressure loss when the HEM is used. In general, the SFM with the two-phase multipliers of Muller–Steinhagen and Heck and Kim and Mudawar outperforms others for channel diameters of less than 3[Formula: see text]mm.

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Yuan, Jide, Hien Quoc Ngo, and Michail Matthaiou. "Machine Learning-Based Channel Prediction in Massive MIMO With Channel Aging." IEEE Transactions on Wireless Communications 19, no. 5 (May 2020): 2960–73. http://dx.doi.org/10.1109/twc.2020.2969627.

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Lee, Juhyeon, and Hyung-Kun Park. "Channel prediction-based channel allocation scheme for multichannel cognitive radio networks." Journal of Communications and Networks 16, no. 2 (April 2014): 209–16. http://dx.doi.org/10.1109/jcn.2014.000032.

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Walpoth, Belinda Nazan, and Burak Erman. "Regulation of ryanodine receptor RyR2 by protein-protein interactions: prediction of a PKA binding site on the N-terminal domain of RyR2 and its relation to disease causing mutations." F1000Research 4 (January 28, 2015): 29. http://dx.doi.org/10.12688/f1000research.5858.1.

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Protein-protein interactions are the key processes responsible for signaling and function in complex networks. Determining the correct binding partners and predicting the ligand binding sites in the absence of experimental data require predictive models. Hybrid models that combine quantitative atomistic calculations with statistical thermodynamics formulations are valuable tools for bioinformatics predictions. We present a hybrid prediction and analysis model for determining putative binding partners and interpreting the resulting correlations in the yet functionally uncharacterized interactions of the ryanodine RyR2 N-terminal domain. Using extensive docking calculations and libraries of hexameric peptides generated from regulator proteins of the RyR2 channel, we show that the residues 318-323 of protein kinase A, PKA, have a very high affinity for the N-terminal of RyR2. Using a coarse grained Elastic Net Model, we show that the binding site lies at the end of a pathway of evolutionarily conserved residues in RyR2. The two disease causing mutations are also on this path. The program for the prediction of the energetically responsive residues by the Elastic Net Model is freely available on request from the corresponding author.

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Et. al., Shilpa P. Khedkar,. "A Deep Learning method for effective channel allotment for SDN based IOT." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 1721–28. http://dx.doi.org/10.17762/turcomat.v12i2.1508.

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Due to advances in the field of internet of things (IoT), the transmission speed become very important and need to be discussed. Doing proper assignment of appropriate channels to the generated traffic in SDN based IoT can affect transmission speed enormously. Software Defined Networking has been evolved as a supporting technology to improve the performance of IoT networks and to increase transmission quality. Different machine learning algorithm can be used for prediction of network traffic and allocation of the channel is done for better assignment. Hence, in this paper CNNs based network traffic prediction and allocation of channel technique is proposed. This technique significantly improves the network performance.

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Mistry, Hitesh B. "Complex versus simple models: ion-channel cardiac toxicity prediction." PeerJ 6 (February 5, 2018): e4352. http://dx.doi.org/10.7717/peerj.4352.

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There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear modelBnetwas conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall theBnetmodel performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

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KIM, H., and S. CHOE. "An Adaptive MIMO-OFDM with Channel Prediction Scheme for Mobile Fading Channels." IEICE Transactions on Communications E91-B, no. 7 (July 1, 2008): 2443–46. http://dx.doi.org/10.1093/ietcom/e91-b.7.2443.

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Al-Samman, A. M., M. H. Azmi, T. A. Rahman, I. Khan, M. N. Hindia, and A. Fattouh. "Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels." PLOS ONE 11, no. 12 (December 19, 2016): e0164944. http://dx.doi.org/10.1371/journal.pone.0164944.

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Xie, Yong Gang, and Dong Ya Shen. "A Novel Hybrid Algorithm for Equalization of WBAN Channel." Applied Mechanics and Materials 543-547 (March 2014): 1868–71. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1868.

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Equalization technology is important in WBAN channels for resolving ISI problem. However, traditional algorithms, such as LMS and RBF are not suitable to be applied in WBAN channel, due to the fact that channels of WBAN are particularly time variable and have severe multipath effect. In this study, we proposed a novel hybrid algorithm by combining NFN and RBFN algorithms. Experimental results show better performance compared to both NFN and RBFN algorithms in prediction problem in WBAN Channels.

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Larsson, Richard, Mathias Milz, Peter Rayer, Roger Saunders, William Bell, Anna Booton, Stefan A. Buehler, Patrick Eriksson, and Viju O. John. "Modeling the Zeeman effect in high-altitude SSMIS channels for numerical weather prediction profiles: comparing a fast model and a line-by-line model." Atmospheric Measurement Techniques 9, no. 2 (March 3, 2016): 841–57. http://dx.doi.org/10.5194/amt-9-841-2016.

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Abstract. We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19–22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to ±7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.

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Journal articles on the topic 'Channel prediction'

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