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Journal articles on the topic 'Method backward propagation algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Li, Jinze, Kaiping Yu, and Hong Tang. "Further Assessment of Three Bathe Algorithms and Implementations for Wave Propagation Problems." International Journal of Structural Stability and Dynamics 21, no. 05 (2021): 2150073. http://dx.doi.org/10.1142/s0219455421500735.

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This paper further analyzes three Bathe algorithms ([Formula: see text]-Bathe, [Formula: see text]-Bathe and [Formula: see text]-Bathe) with their unknown properties revealed. The analysis shows firstly that three Bathe algorithms can cover two common integration schemes, trapezoidal rule and backward Euler formula, and that the second-order [Formula: see text]-Bathe algorithm is algebraically identical to the [Formula: see text]-Bathe algorithm. Via formulation of the generalized two-sub-step Newmark algorithm, it is shown that the common Newmark method cannot be considered as a special case

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Felix, A. Pyatakovich* Lubov V. Khlivnenko Tatyana I. Yakunchenko Kristina F. Makkonen Olga V. Mevsha. "A COMPARATIVE ANALYSIS OF THE RESULTS OF CONVENTIONAL AND COMBINED METHODS OF TRAINING DIRECT PROPAGATION NEURAL NETWORK IN HEALTHY PERSONS TO DETECTING THE DEGREE OF ACTIVITY OF AN AUTONOMOUS NERVOUS SYSTEM." Indo American Journal of Pharmaceutical Sciences 04, no. 09 (2017): 3075–79. https://doi.org/10.5281/zenodo.910741.

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The article deals with a comparative analysis of the efficiency of an artificial neural network (ANN) trained with the help of algorithm back propagation, and of that trained by combining a back propagation algorithm and a variant of Cauchy stochastic training, in detecting the degree of activity of an autonomous nervous system. For the purposes realization of the project has been developed a biotechnical system, including technical device for input electrophysiological information in mode on-line. To evaluate the clinical effectiveness of the classification, records of interpulse intervals in

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Zhao, Changtong, Jie Ma, Wenshen Jia, et al. "An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm." Biosensors 12, no. 9 (2022): 692. http://dx.doi.org/10.3390/bios12090692.

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To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization–back-propagation neural network (PSO-BPNN), a gray wolf optimization–backward propagation neural network (GWO-BPNN)

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Lisitsyna, Liubov, Marina Senchilo, and Sergei Teleshev. "CREATING RLCP-COMPATIBLE VIRTUAL LABORATORIES FOR TRAINING BASIC ALGORITHMS ON NEURAL NETWORKS." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 3 (2021): 134–42. http://dx.doi.org/10.24143/2072-9502-2021-3-134-142.

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The article describes the principles of developing RLCP-compatible virtual laboratories. There are build two virtual laboratories based on these principles for mastering the basic algo-rithms on neural networks: Algorithm for Sequential Signal Propagation in Perceptron and Algorithm for Training Perceptron Using Method of Backward Error Propagation. Virtual laboratories consist of two independent modules – a virtual stand and an RLCP server. The virtual stand implements a visual display of the task's data and provides the listener with tools for forming and editing intermediate solutions and r

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Hwang, Taewook, Hyein Seo, and Sangkeun Jung. "Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training." Applied Sciences 15, no. 15 (2025): 8207. https://doi.org/10.3390/app15158207.

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Recent deep learning models, including GPT-4, have achieved remarkable performance using the back-propagation (BP) algorithm. However, the mechanism of BP is fundamentally different from how the human brain processes learning. To address this discrepancy, the Forward-Forward (FF) algorithm was introduced. Although FF enables deep learning without backward passes, it suffers from instability, dependence on artificial input construction, and limited generalizability. To overcome these challenges, we propose Local Back-Propagation (LBP), a method that integrates layer-wise unsupervised learning w

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Shperberg, Shahaf, Ariel Felner, Solomon Shimony, Nathan Sturtevant, and Avi Hayoun. "Improving Bidirectional Heuristic Search by Bounds Propagation." Proceedings of the International Symposium on Combinatorial Search 10, no. 1 (2021): 106–14. http://dx.doi.org/10.1609/socs.v10i1.18508.

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Recent work in bidirectional heuristic search characterize pairs of nodes from which at least one node must be expanded in order to ensure optimality of solutions. We use these findings to propose a method for improving existing heuristics by propagating lower bounds between the forward and backward frontiers. We then define a number of desirable properties for bidirectional heuristic search algorithms, and show that applying the bound propagations adds these properties to many existing algorithms (e.g. to the MM family of algorithms). Finally, experimental results show that applying these pro

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Брыкин, В. В., М. Я. Брагинский, Д. В. Тараканов, and И. Л. Назарова. "CLASSIFYING PLANTS’ HEALTH USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)." Proceedings in Cybernetics 23, no. 2 (2024): 23–30. http://dx.doi.org/10.35266/1999-7604-2024-2-3.

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The study classifi es eight types of plants’ diseases using an adaptive neuro-fuzzy inference system (ANFIS). Haralick texture features obtained from plants’ images are applied as input data for a system. A hybrid algorithm consisting of a backward propagation of error and a gradient descent performed the ANFIS training. The ANFIS effi ciency was assessed on a test set through calculating accuracy, comprehensiveness, and the F1 score. The indicators obtained by this method were compared with other modern classifi cation methods.

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WANG, SHILONG, FEI ZHENG, and LING XU. "COMPARISON BETWEEN PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM IN ARTIFICIAL NEURAL NETWORK FOR LIFE PREDICTION OF NC TOOLS." Journal of Advanced Manufacturing Systems 07, no. 01 (2008): 1–7. http://dx.doi.org/10.1142/s0219686708001073.

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Accurate life prediction of NC (Numeric Control) tools is very essential in an advanced manufacturing system. In this paper, tool life prediction in a drilling process was researched. An Artificial Neural Network (ANN) has been established for prediction, with drill diameter, cutting speed and feed rate as input parameters and tool life as an output parameter. To improve the performance of the network, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied independently to train the network instead of standard Backward Propagation (BP) algorithm, which has drawbacks of low c

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Zhang, Bolun, Guangen Gao, and Yongxin Gao. "Cooperative Localization Based on Augmented State Belief Propagation for Mobile Agent Networks." Electronics 11, no. 13 (2022): 1959. http://dx.doi.org/10.3390/electronics11131959.

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Belief propagation (BP) is widely used to solve the cooperative localization problem due to its excellent performance and natural distributed structure of implementation. For a mobile agent network, its factor graph inevitably encounters loops. In this case, the BP algorithm becomes iterative and can only provide an approximate marginal probability density function of the estimate with finite iterations. We propose an augmented-state BP algorithm for mobile agent networks to alleviate the effect of loops. By performing state augmentation, the messages in the factor graph will actually be allow

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Yang, Pengliang, Romain Brossier, Ludovic Métivier, and Jean Virieux. "Wavefield reconstruction in attenuating media: A checkpointing-assisted reverse-forward simulation method." GEOPHYSICS 81, no. 6 (2016): R349—R362. http://dx.doi.org/10.1190/geo2016-0082.1.

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Three-dimensional implementations of reverse time migration (RTM) and full-waveform inversion (FWI) require efficient schemes to access the incident field to apply the imaging condition of RTM or build the gradient of FWI. Wavefield reconstruction by reverse propagation using final snapshot and saved boundaries appears quite efficient but unstable in attenuating media, whereas the checkpointing strategy is a stable alternative at the expense of increased computational cost through repeated forward modeling. We have developed a checkpointing-assisted reverse-forward simulation (CARFS) method in

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Zhang, Lin, and Kan Li. "Influence Maximization Based on Backward Reasoning in Online Social Networks." Mathematics 9, no. 24 (2021): 3189. http://dx.doi.org/10.3390/math9243189.

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Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to mining the most influential top-K nodes from an online social network to maximize the final propagation of influence in the network. The existing studies have shown that the greedy algorithms can obtain a highly accurate result, but its calculation is time-consuming. Although heuristic algorithms can improve efficie

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WANG, HSIAO-FAN, and CHING-YI KUO. "THREE-PARAMETER FUZZY ARITHMETIC APPROXIMATION OF L-R FUZZY NUMBERS FOR FUZZY NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 02 (2006): 211–33. http://dx.doi.org/10.1142/s0218488506003959.

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In this study, we proposed an alternative operation of fuzzy arithmetic on L-R fuzzy numbers by three parameters of mode, left spread and right spread. Then, based on this approximation method, a new learning algorithm of a fully fuzzified neural network was developed in which the L-R fuzzy numbers were considered as the fuzzy signals. While the forward operations of fuzzy signals were based on the proposed three-parameter fuzzy arithmetic approximation method, the backward learning adopted a back-propagation learning procedure with a measurable error function. The learning algorithm was illus

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Jerome, J. "Efficient Reactive Power Compensation Algorithm for Distribution Network." ASEAN Journal on Science and Technology for Development 20, no. 3&4 (2017): 373–84. http://dx.doi.org/10.29037/ajstd.358.

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The use of automation and energy efficient equipment with electronic control would greatly improve industrial production. These new devices are more sensitive to supply voltage deviation and the characteristics of the power system that was previously ignored are now very important. Hence the benefits of distribution automation have been widely acknowledged in recent years. This paper proposes an efficient load flow solution technique extended to find optimum location for reactive power compensation and network reconfiguration for planning and day-to-day operation of distribution networks. This

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Wei, Wu, Xu Le, Zhang Xiaofei, and Li Jianfeng. "Compressed Sensing PARALIND Decomposition-Based Coherent Angle Estimation for Uniform Rectangular Array." Wireless Communications and Mobile Computing 2019 (June 24, 2019): 1–10. http://dx.doi.org/10.1155/2019/3619818.

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In this paper, the topic of coherent two-dimensional direction of arrival (2D-DOA) estimation is investigated. Our study jointly utilizes the compressed sensing (CS) technique and the parallel profiles with linear dependencies (PARALIND) model and presents a 2D-DOA estimation algorithm for coherent sources with the uniform rectangular array. Compared to the traditional PARALIND decomposition, the proposed algorithm owns lower computational complexity and smaller data storage capacity due to the process of compression. Besides, the proposed algorithm can obtain autopaired azimuth angles and ele

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Liu, Shuo, Xu Chen, and Xuan Di. "Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator." Mathematics 12, no. 6 (2024): 803. http://dx.doi.org/10.3390/math12060803.

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This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of population dynamics and a backward PDE for a representative agent’s optimal control. Existing work mainly focus on solving the mean field game equilibrium (MFE) of the MFG system when given fixed boundary conditions, including the initial population state and terminal cost. To obtain MFE efficiently, particularly when the initial population density and ter

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Sebayang, Ika Sari Damayanthi, Agus Suroso, and Alnis Gustin Laoli. "OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD." SINERGI 22, no. 3 (2018): 193. http://dx.doi.org/10.22441/sinergi.2018.3.008.

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The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date. In actual fact the relationship of rainfall-runoff is known to be highly non-linear and complex. The spatial and temporal precipitation patterns and the variability of watershed characteristics create a more complex hydrologic phenomenon. Runoff is part of the rain water that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network

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Li, Shaoqian, Zhenyuan Zhang, Yang Liu, and Zixia Qin. "A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety." Mathematical Problems in Engineering 2020 (December 9, 2020): 1–9. http://dx.doi.org/10.1155/2020/6682216.

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With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selecti

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Xie, Hui, Weiyu Xu, Ya Xing Wang, John Buatti, and Xiaodong Wu. "gcDLSeg: integrating graph-cut into deep learning for binary semantic segmentation." Biomedical Optics Express 16, no. 5 (2025): 1999. https://doi.org/10.1364/boe.555206.

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Binary semantic segmentation in computer vision is a fundamental problem. As a model-based segmentation method, the graph-cut approach was one of the most successful binary segmentation methods thanks to its global optimality guarantee of the solutions and its practical polynomial-time complexity. Recently, many deep learning (DL) based methods have been developed for this task and yielded remarkable performance, resulting in a paradigm shift in this field. To combine the strengths of both approaches, we propose in this study to integrate the graph-cut approach into a deep learning network for

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Quan, Jiale, Binbin Yan, Xinzhu Sang, et al. "Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction." Micromachines 14, no. 3 (2023): 605. http://dx.doi.org/10.3390/mi14030605.

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In this paper, we propose a method to generate multi-depth phase-only holograms using stochastic gradient descent (SGD) algorithm with weighted complex loss function and masked multi-layer diffraction. The 3D scene can be represented by a combination of layers in different depths. In the wave propagation procedure of multiple layers in different depths, the complex amplitude of layers in different depths will gradually diffuse and produce occlusion at another layer. To solve this occlusion problem, a mask is used in the process of layers diffracting. Whether it is forward wave propagation or b

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Wu, Yingnian, Lin Zhang, and Lan Mu. "Electromagnetic Computation and Visualization of Transmission Particle Model and Its Simulation Based on GPU." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/942106.

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Electromagnetic calculation plays an important role in both military and civic fields. Some methods and models proposed for calculation of electromagnetic wave propagation in a large range bring heavy burden in CPU computation and also require huge amount of memory. Using the GPU to accelerate computation and visualization can reduce the computational burden on the CPU. Based on forward ray-tracing method, a transmission particle model (TPM) for calculating electromagnetic field is presented to combine the particle method. The movement of a particle obeys the principle of the propagation of el

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Khaldirian, M. F., A. P. Rahardjo, D. Luknanto, and R. D. R. Sondi. "An Alternative Algorithm for Simulating Flash Flood." IOP Conference Series: Earth and Environmental Science 930, no. 1 (2021): 012076. http://dx.doi.org/10.1088/1755-1315/930/1/012076.

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Abstract Most of the approaches in numerical modeling techniques are based on the Eulerian coordinate system. This approach faces difficulty in simulating flash flood front propagation. This paper shows an alternative method that implements a numerical modeling technique based on the Lagrangian coordinate system to simulate the water of debris flow. As for the interaction with the riverbed, the simulation uses an Eulerian coordinate system. The method uses the conservative and momentum equations of water and sediment mixture in the Lagrangian form. Source terms represent deposition and erosion

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Wang, Zijian, and Pizhong Qiao. "Backward wave separation method in a single transmitter and multi-receiver sensor array for improved damage identification of two-dimensional structures." International Journal of Damage Mechanics 26, no. 2 (2017): 229–50. http://dx.doi.org/10.1177/1056789517694477.

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In this paper, a backward wave separation method is proposed. Since the first backward wave can be considered as the damage reflection, the damage reflected wave peaks are artificially amplified by appropriately shifting and summing signals collected from a series of sensing points aligned along the wave propagating direction. This shifting and summing process aims to present the damage reflected wave peaks more distinctively while offsets randomly distributed environmental interferences. Due to the high signal-to-noise ratio of the treated signal, the application of backward wave separation i

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Kim, Young Seo, Ali Almomin, Woodon Jeong, and Constantine Tsingas. "Least-squares reverse time migration using analytic-signal-based wavefield decomposition." GEOPHYSICS 84, no. 3 (2019): S149—S157. http://dx.doi.org/10.1190/geo2018-0118.1.

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An intrinsic problem during migration and imaging of seismic wavefields using the two-way wave equation is the crosstalk interference between the up/down propagation of the corresponding source and receiver wavefields. To mitigate this crosstalk, the downgoing source and upgoing receiver wavefield imaging condition (IC) is adopted at an early stage of the inversion process, improving convergence and obtaining cleaner reflection images. A wavefield decomposition methodology can also be incorporated into a least-squares reverse time migration (LSRTM) algorithm. The separation of wavefields based

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Zhu, Yaolin, Jiameng Duan, and Tong Wu. "Animal fiber imagery classification using a combination of random forest and deep learning methods." Journal of Engineered Fibers and Fabrics 16 (January 2021): 155892502110093. http://dx.doi.org/10.1177/15589250211009333.

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Feature extraction is a key step in animal fiber microscopic images recognition that plays an important role in the wool industry and textile industry. To improve the accuracy of wool and cashmere microscopic images classification, a hybrid model based on Convolutional Neural Network (CNN) and Random Forest (RF) is proposed for automatic feature extraction and classification of animal fiber microscopic images. First, use CNN to learn the representative high-level features from animal fiber images, then add dropout layers to avoid over-fitting. And the backward propagation algorithm are used to

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Liu, Hongwei, Almomin Ali, and Yi Luo. "Reverse time adjoint migration." GEOPHYSICS 84, no. 3 (2019): R401—R410. http://dx.doi.org/10.1190/geo2018-0381.1.

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From a mathematical perspective, it is desirable to apply the adjoint operator for back propagating the receiver wavefield for migration of data residuals for inversion. For frequency-domain direct solvers, it is straightforward to apply the adjoint operator, whereas in most real applications of time-domain finite-difference (TDFD) stencils, the forward-propagation kernel function is reused for backward propagation for simplicity. However, when applying the exact adjoint operator, the migration result will be different, especially when strong variations exist in the velocity model. Actually, t

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Cui, Taoyong, and Yuhan Dong. "Simple Orthogonal Graph Representation Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23462–64. http://dx.doi.org/10.1609/aaai.v38i21.30430.

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Graph neural networks (GNNs) have attracted significant interest recently since they can effectively process and analyze graph-structured data commonly found in real-world applications. However, the predicament that GNNs are difficult to train becomes worse as the layers increase. The essence of this problem is that stacking layers will reduce the stability of forward propagation and gradient back-propagation. And as the increasing scale of models (measured by the number of parameters), how to efficiently and effectively adapt it to particular downstream tasks becomes an intriguing research is

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Naresh Manandhar, Shree Raj Shakya, and Sudip Bhattrai. "Optimal network reconfiguration and distributed generation integration for power loss minimization and voltage profile enhancement in radial distribution system." Journal of Innovations in Engineering Education 6, no. 1 (2023): 154–63. http://dx.doi.org/10.3126/jiee.v6i1.48006.

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Network Reconfiguration with Distributed Generation (DG) integration can significantly minimize the power loss and enhance the voltage profile. This paper presents power loss minimization and voltage profile enhancement measures in radial distribution systems through optimal network reconfiguration and DG integration. Initially, the approach was tested in a standard IEEE 33-Bus system in MATLAB using the Backward/Forward Propagation Power Flow method. Voltage Stability Index (VSI) was used to determine the candidate bus to locate for DG integration. Artificial Bee Colony (ABC) algorithm was us

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Moysis, Lazaros, and Nicholas P. Karampetakis. "Construction of Algebraic and Difference Equations with a Prescribed Solution Space." International Journal of Applied Mathematics and Computer Science 27, no. 1 (2017): 19–32. http://dx.doi.org/10.1515/amcs-2017-0002.

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Abstract This paper studies the solution space of systems of algebraic and difference equations, given as auto-regressive (AR) representations A(σ)β(k) = 0, where σ denotes the shift forward operator and A(σ) is a regular polynomial matrix. The solution space of such systems consists of forward and backward propagating solutions, over a finite time horizon. This solution space can be constructed from knowledge of the finite and infinite elementary divisor structure of A(σ). This work deals with the inverse problem of constructing a family of polynomial matrices A(σ) such that the system A(σ)β(

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Gauthier, Odile, Jean Virieux, and Albert Tarantola. "Two‐dimensional nonlinear inversion of seismic waveforms: Numerical results." GEOPHYSICS 51, no. 7 (1986): 1387–403. http://dx.doi.org/10.1190/1.1442188.

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The nonlinear problem of inversion of seismic waveforms can be set up using least‐squares methods. The inverse problem is then reduced to the problem of minimizing a lp;nonquadratic) function in a space of many [Formula: see text] variables. Using gradient methods leads to iterative algorithms, each iteration implying a forward propagation generated by the actual sources, a backward propagation generated by the data residuals (acting as if they were sources), and a correlation at each point of the space of the two fields thus obtained, which gives the updated model. The quality of the results

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Pecev, Predrag, and Milos Rackovic. "LTR-MDTS structure - a structure for multiple dependent time series prediction." Computer Science and Information Systems 14, no. 2 (2017): 467–90. http://dx.doi.org/10.2298/csis150815004p.

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The subject of research presented in this paper is to model a neural network structure and appropriate training algorithm that is most suited for multiple dependent time series prediction / deduction. The basic idea is to take advantage of neural networks in solving the problem of prediction of synchronized basketball referees? movement during a basketball action. Presentation of time series stemming from the aforementioned problem, by using traditional Multilayered Perceptron neural networks (MLP), leads to a sort of paradox of backward time lapse effect that certain input and hidden layers n

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Sampath, Karthick, Subburayan Veerasamy, and Ravi P. Agarwal. "Fractional-Step Method with Interpolation for Solving a System of First-Order 2D Hyperbolic Delay Differential Equations." Computation 11, no. 3 (2023): 57. http://dx.doi.org/10.3390/computation11030057.

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In this article, we consider a delayed system of first-order hyperbolic differential equations. The presence of the delay term in first-order hyperbolic delay differential equations poses significant challenges in both analysis and numerical solutions. The delay term also makes it more difficult to use standard numerical methods for solving differential equations, as these methods often require that the differential equation be evaluated at the current time step. To overcome these challenges, specialized numerical methods and analytical techniques have been developed for solving first-order hy

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VORONOVICH, A. "NON-PARABOLIC MARCHING ALGORITHM FOR SOUND FIELD CALCULATION IN THE OCEAN WAVEGUIDE." Journal of Computational Acoustics 04, no. 04 (1996): 399–423. http://dx.doi.org/10.1142/s0218396x96000155.

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An algorithm is presented for calculating sound field in the inhomogeneous ocean waveguide. It does not involve parabolic approximation and can be considered as principally exact (at least for 2D inhomogeneities of the sound speed field). On the other hand, it is “marching” and can be easily implemented as a computer code (note, that marching in this case proceeds in “backward” direction, i.e. towards the source). Those features of the code are similar to couple mode algorithm (COUPLE) developed originally by R. Evans. The principal difference is that suggested code does not assume piecewise c

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Sakai, Yusuke, Shinya Yamada, Toshiki Sato, Ryota Hayakawa, Ryota Higurashi, and Nao Kominato. "Richardson–Lucy Deconvolution with a Spatially Variant Point-spread Function of Chandra: Supernova Remnant Cassiopeia A as an Example." Astrophysical Journal 951, no. 1 (2023): 59. http://dx.doi.org/10.3847/1538-4357/acd9b3.

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Abstract Richardson–Lucy (RL) deconvolution is one of the classical methods widely used in X-ray astronomy and other areas. Amid recent progress in image processing, RL deconvolution still leaves much room for improvement under realistic situations. One direction is to include the positional dependence of a point-spread function (PSF), so-called RL deconvolution with a spatially variant PSF (RLsv). Another is the method of estimating a reliable number of iterations and their associated uncertainties. We developed a practical method that incorporates the RLsv algorithm and the estimation of unc

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Zheng, Die. "Research on the Evaluation Model of Educational Management Theory Based on Data Mining from the Perspective of Neural Network." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/7260806.

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The improvement of the theoretical quality of education management is an indispensable part of a country’s education modernization. However, the existing research on the evaluation of educational management theory is still relatively small, and there is a lack of scientific educational management theory evaluation model. Designing a comprehensive and accurate educational management theory evaluation model has important theoretical value and practical significance. It is possible to process a lot of information in parallel using the artificial neural network method. By optimizing the artificial

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Zhang, Huihui, Hugo A. Loáiciga, Da Ha, and Qingyun Du. "Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms." Journal of Hydrometeorology 21, no. 6 (2020): 1259–78. http://dx.doi.org/10.1175/jhm-d-19-0289.1.

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AbstractTropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment f

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Aqil, M., I. Kita, A. Yano, and S. Nishiyama. "Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system." Water Science and Technology 54, no. 11-12 (2006): 11–19. http://dx.doi.org/10.2166/wst.2006.839.

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It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of th

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Hoła, Anna, and Łukasz Sadowski. "Verification of a Nondestructive Method for Assessing the Humidity of Saline Brick Walls in Historical Buildings." Applied Sciences 10, no. 19 (2020): 6926. http://dx.doi.org/10.3390/app10196926.

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The paper presents the results of the verification of the neural method for assessing the humidity of saline brick walls. The method was previously developed by the authors and can be useful for the nondestructive assessment of the humidity of walls in historic buildings when destructive intervention during testing is not possible due to conservation restrictions. However, before being implemented in construction practice, this method requires validation by verification on other historic buildings, which to date has not been done. The paper presents the results of such verification, which has

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Yoshikawa, Eiichi, V. Chandrasekar, and Tomoo Ushio. "Raindrop Size Distribution (DSD) Retrieval for X-Band Dual-Polarization Radar." Journal of Atmospheric and Oceanic Technology 31, no. 2 (2014): 387–403. http://dx.doi.org/10.1175/jtech-d-12-00248.1.

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Abstract A raindrop size distribution (DSD) retrieval method for a dual-polarization radar at attenuating frequency is proposed. The proposed method is developed such that the range profiles of the gamma DSD parameters, an intercept parameter Nw (mm−1 m−3), and a median volume diameter D0 (mm) can be estimated to match the dual-polarization measurements, measured equivalent reflectivity at horizontal polarization ZHm, measured differential reflectivity ZDRm, and measured differential propagation phase ΦDPm, where the forward scattering and backscattering are formulated simultaneously to avoid

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Xu, Yang, Bin Zhu, Zheng Zhang, and Jiahui Chen. "Hierarchical Dynamic Bayesian Network-Based Fatigue Crack Propagation Modeling Considering Initial Defects." Sensors 22, no. 18 (2022): 6777. http://dx.doi.org/10.3390/s22186777.

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Orthotropic steel decks (OSDs) are inevitably subjected to fatigue damage caused by cycled vehicle loads in long-span bridges. This study establishes a probabilistic analysis framework integrating the dynamic Bayesian network (DBN) and fracture mechanics to model the fatigue crack propagation considering mutual correlations among multiple fatigue details. Both the observations of fatigue crack length from field inspection and monitoring data of vehicle loads from the weight-in-motion (WIM) system are utilized. First, fracture mechanics-based uncertainty analysis is performed to determine the m

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Sychugov, Anton, Vadim Miheychikov, and Maksim Chernyshov. "Application of Neural Networks for Object Recognition in Railway Transportation." Proceedings of Petersburg Transport University 20, no. 2 (2023): 478–91. http://dx.doi.org/10.20295/1815-588x-2023-2-478-491.

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Purpose: With the help of vision systems and neural networks, such as YOLOv8 and MASK R-CNN, it is possible to quickly and accurately detect objects that can lead to an accident or delay trains. YOLOv8 is one of the most popular real-time object detection algorithms that uses deep neural networks to classify and localize objects. YOLOv8 can detect objects in images and videos with high speed and accuracy. This model can work on various hardware platforms, including mobile devices and computers. MASK R-CNN is an even more advanced object detection algorithm that has the ability to highlight obj

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Ma, Xu, Lei Lu, Jianli Ding, Fei Zhang, and Baozhong He. "Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image." Remote Sensing 13, no. 19 (2021): 3874. http://dx.doi.org/10.3390/rs13193874.

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With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is nece

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Niu, Yifan, and Weihong Deng. "Federated Learning for Face Recognition with Gradient Correction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1999–2007. http://dx.doi.org/10.1609/aaai.v36i2.20095.

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With increasing appealing to privacy issues in face recognition, federated learning has emerged as one of the most prevalent approaches to study the unconstrained face recognition problem with private decentralized data. However, conventional decentralized federated algorithm sharing whole parameters of networks among clients suffers from privacy leakage in face recognition scene. In this work, we introduce a framework, FedGC, to tackle federated learning for face recognition and guarantees higher privacy. We explore a novel idea of correcting gradients from the perspective of backward propaga

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Mikhalychev, Alexander, Andrei Benediktovitch, Tatjana Ulyanenkova, and Alex Ulyanenkov. "Ab initiosimulation of diffractometer instrumental function for high-resolution X-ray diffraction." Journal of Applied Crystallography 48, no. 3 (2015): 679–89. http://dx.doi.org/10.1107/s1600576715006986.

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Modeling of the X-ray diffractometer instrumental function for a given optics configuration is important both for planning experiments and for the analysis of measured data. A fast and universal method for instrumental function simulation, suitable for fully automated computer realization and describing both coplanar and noncoplanar measurement geometries for any combination of X-ray optical elements, is proposed. The method can be identified as semi-analytical backward ray tracing and is based on the calculation of a detected signal as an integral of X-ray intensities for all the rays reachin

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Kao, Yu-Wei, and Hung-Hsuan Chen. "Associated Learning: Decomposing End-to-End Backpropagation Based on Autoencoders and Target Propagation." Neural Computation 33, no. 1 (2021): 174–93. http://dx.doi.org/10.1162/neco_a_01335.

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Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient partially because of backward locking, which means updating the weights of one layer locks the weight updates in the other layers. Consequently, it is challenging to apply parallel computing or a pipeline structure to update the weights in different layers simultaneously. In this letter, we introduce a novel learning structure, associated learning (AL), that modularizes the network into smaller components, each of which has a local objective. Because the objectives are mutually independent, AL ca

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Deng, Xiang Bo, Yue Xiang Lin, Ling Dong Bu, Li Zhou Zhang, and Zhe Liu. "Prediction Modeling of Maximum Dry Density of Coarse Grained Soil Using Improved Artificial Neural Networks." Applied Mechanics and Materials 405-408 (September 2013): 24–29. http://dx.doi.org/10.4028/www.scientific.net/amm.405-408.24.

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To characterize the coarse-grained soil of the fillers in Zhijiang North station of Shanghai-Kunming Passenger Dedicated Line, the vibration compaction test was conducted to study maximum dry densities under different granular compositions. Considering the non-linear relations between granular compositions and maximum dry densities, a BP neural network prediction model of which the input layer was consisted of granular compositions, grading index and fractal index was established based on the backwards error propagation algorithm and the results of the maximum dry density tests. As was improve

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Kang, Seung-Goo, and U. Geun Jang. "Frequency-Domain Reverse-Time Migration with Analytic Green’s Function for the Seismic Imaging of Shallow Water Column Structures in the Arctic Ocean." Sensors 23, no. 14 (2023): 6622. http://dx.doi.org/10.3390/s23146622.

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Seismic oceanography can provide a two- or three-dimensional view of the water column thermocline structure at a vertical and horizontal resolution from the multi-channel seismic dataset. Several seismic imaging methods and techniques for seismic oceanography have been presented in previous research. In this study, we suggest a new formulation of the frequency-domain reverse-time migration method for seismic oceanography based on the analytic Green’s function. For imaging thermocline structures in the water column from the seismic data, our proposed seismic reverse-time migration method uses t

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Qin, Xujiang, Qi He, Xin Zhang, and Xiang Yang. "Data Value Assessment in Digital Economy Based on Backpropagation Neural Network Optimized by Genetic Algorithm." Symmetry 17, no. 5 (2025): 761. https://doi.org/10.3390/sym17050761.

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As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges due to its nonlinear nature and the instability of neural networks, including gradient vanishing, parameter sensitivity, and slow convergence. To overcome these challenges, this study proposes a genetic algorithm-optimized BP (GA-BP) model, enhancing the efficiency and accuracy of data valuation

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Chen, Zhulin, Kun Jia, Chenchao Xiao, et al. "Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods." Remote Sensing 12, no. 13 (2020): 2110. http://dx.doi.org/10.3390/rs12132110.

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Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redu

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Guo, Qi, Daozong Sun, Zhen Li, Shilei Lyu, and Xiuyun Xue. "Multitargets Orientation Technique Based on Reflection Characteristic Analysis Using an Inverse Diffraction Parabolic Equation." International Journal of Antennas and Propagation 2024 (January 5, 2024): 1–10. http://dx.doi.org/10.1155/2024/5846526.

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In this article, the inverse diffraction parabolic equation (IDPE) model based on the finite difference method is proposed, which is first applied in the multiple nonradiation targets orientation technology. In principle, the electromagnetic signal propagating in the transmission path will produce a reflected signal back to the source end while encountering the discontinuous objects. The distribution of the reflection or refraction intensity is directly associated with the distances and heights of the objects, so the location can be determined by means of analyzing the distribution. Here, acco

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Trzepieciński, Tomasz, Marcin Szpunar, and Ľuboš Kaščák. "Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks." Materials 14, no. 10 (2021): 2570. http://dx.doi.org/10.3390/ma14102570.

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This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: th

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