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Journal articles on the topic 'Gradient BP based approach'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Ding, Feng, and Xing Ben Han. "Data-Driven Approach for Equipment Reliability Prediction Using Neural Network." Advanced Materials Research 411 (November 2011): 563–66. http://dx.doi.org/10.4028/www.scientific.net/amr.411.563.

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BP neural network based data-driven method is proposed to predict reliability in this paper. The BP neural network prediction using Gradient Descent Method (GDM), Additional Momentum Gradient Descent Method (AMGDM) and Levenberg-Marquardt Method(L-M) based on numerical optimization theory of training algorithm are compared with different neuron number. The proposed approach is validated via age data collected from computer numerical control (CNC) machine tool in the field. The results from the proposed method show that perfect predicting performance is achieved under considering selecting suit

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Chaurasia, Satvik, and R. Shobana. "An Adaptive Growing Pruning Algorithm to Optimize Dynamic Feed Forward Neural Networks for Nonlinear Dynamic System Identification." International Journal of Microsystems and IoT 3, no. 1 (2025): 1519–25. https://doi.org/10.5281/zenodo.15493712.

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Optimizing the structure is very crucial for effective identification and control of any nonlinear system. Optimization leads to a robust and more generalized structure. In this work, an effective adaptive growing-pruning algorithm scheme is proposed to optimize dynamic feed forward structures. The hidden layer of the static FFNN is made dynamic and the weights of the dynamic FFNN are trained using standard Back propagation algorithm. Firstly, the network is grown only when the MSE is found high and increasing. Likewise, unnecessarily neurons are pruned based on low activation variance. The le

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Wang, Youming, and Didi Qing. "Model Predictive Control of Nonlinear System Based on GA-RBP Neural Network and Improved Gradient Descent Method." Complexity 2021 (March 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/6622149.

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A model predictive control (MPC) method based on recursive backpropagation (RBP) neural network and genetic algorithm (GA) is proposed for a class of nonlinear systems with time delays and uncertainties. In the offline modeling stage, a multistep-ahead predictor with GA-RBP neural network is designed, where GA-BP neural network is used as a one-step prediction model and GA is employed to train the initial weights and bias of the BP neural network. The incorporation of GA into RBP can reduce the possibility of the BP neural network falling into a local optimum instead of reaching global optimiz

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Yantsevich, Aleksei V., Veronika V. Shchur, and Sergey A. Usanov. "Oligonucleotide Preparation Approach for Assembly of DNA Synthons." SLAS TECHNOLOGY: Translating Life Sciences Innovation 24, no. 6 (2019): 556–68. http://dx.doi.org/10.1177/2472630319850534.

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An effective oligonucleotide preparation approach for the thermodynamically balanced, inside-out (TBIO) PCR-based assembly of long synthetic DNA molecules (synthons) is described in the current work. We replaced the necessity to purify individual oligonucleotides with just one purification procedure per approximately 500 base pairs (bp) of duplex DNA. So for an enhanced green fluorescent protein (EGFP) gene of 717 bp, we synthesized 24 oligonucleotides with a length of 50 bases and performed just two solid-phase extraction (SPE) purification procedures. It was found that the capacity of ZipTip

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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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Singh, Garima, and Laxmi Srivastava. "Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment." Advances in Artificial Neural Systems 2011 (July 31, 2011): 1–9. http://dx.doi.org/10.1155/2011/532785.

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With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN) has been proposed for voltage stability margin estimation which is an indication of the power system's

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Alkawaz, Ali Najem, Jeevan Kanesan, Anis Salwa Mohd Khairuddin, et al. "Training Multilayer Neural Network Based on Optimal Control Theory for Limited Computational Resources." Mathematics 11, no. 3 (2023): 778. http://dx.doi.org/10.3390/math11030778.

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Backpropagation (BP)-based gradient descent is the general approach to train a neural network with a multilayer perceptron. However, BP is inherently slow in learning, and it sometimes traps at local minima, mainly due to a constant learning rate. This pre-fixed learning rate regularly leads the BP network towards an unsuccessful stochastic steepest descent. Therefore, to overcome the limitation of BP, this work addresses an improved method of training the neural network based on optimal control (OC) theory. State equations in optimal control represent the BP neural network’s weights and biase

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Пахомова, Е. А., А. В. Пахомов, and А. В. Щеголев. "BASIS FOR THE PRACTICAL IMPLEMENTATION OF THE BALANCE OF PAYMENT CURVE AS A TASK FOR THE ECONOMIC ENVIRONMENT IN THE RUSSIAN MODIFICATION OF THE TRIPLE HELIX MODEL." Audit and Financial Analysis, no. 01_2022 (March 3, 2022): 11–16. http://dx.doi.org/10.38097/afa.2022.70.84.003.

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Статья посвящена одной из задач экономического окружения в авторской модификации модели тройной спирали в условиях российской экономики – практической реализации кривой платежного баланса BP как компоненты модели IS-LM-BP. Впервые представлен методический подход к основам практической реализации этой компоненты, основанный на принципе предметно-ориентированной декомпозиции, с анализом декомпозиционных компонент методами корреляционно-регрессионного анализа, Крамера, приведенного градиента. The article is devoted to one of the tasks of the economic environment in the author's modification of th

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Yusong, Liu, Su Zhixun, Zhang Bingjie, Gong Xiaoling, and Sang Zhaoyang. "Convergence Analysis of An Improved Extreme Learning Machine Based on Gradient Descent Method." Journal of Applied Computer Science Methods 8, no. 1 (2016): 5–15. http://dx.doi.org/10.1515/jacsm-2016-0001.

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Abstract Extreme learning machine (ELM) is an efficient algorithm, but it requires more hidden nodes than the BP algorithms to reach the matched performance. Recently, an efficient learning algorithm, the upper-layer-solution-unaware algorithm (USUA), is proposed for the single-hidden layer feed-forward neural network. It needs less number of hidden nodes and testing time than ELM. In this paper, we mainly give the theoretical analysis for USUA. Theoretical results show that the error function monotonously decreases in the training procedure, the gradient of the error function with respect to

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Riedel, Jon L., Alice Telka, Andy Bunn, and John J. Clague. "Reconstruction of climate and ecology of Skagit Valley, Washington, from 27.7 to 19.8 ka based on plant and beetle macrofossils." Quaternary Research 106 (October 27, 2021): 94–112. http://dx.doi.org/10.1017/qua.2021.50.

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AbstractGlacial lake sediments exposed at two sites in Skagit Valley, Washington, encase abundant macrofossils dating from 27.7 to 19.8 cal ka BP. At the last glacial maximum (LGM) most of the valley floor was part of a regionally extensive arid boreal (subalpine) forest that periodically included montane and temperate trees and open boreal species such as dwarf birch, northern spikemoss, and heath. We used the modern distribution and climate of 14 species in 12 macrofossil assemblages and a probability density function approach to reconstruct the LGM climate. Median annual precipitation (MAP)

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Zhang, Zhenhao, Fan Feng, and Jie Liu. "Characterizing collaborative transcription regulation with a graph-based deep learning approach." PLOS Computational Biology 18, no. 6 (2022): e1010162. http://dx.doi.org/10.1371/journal.pcbi.1010162.

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Human epigenome and transcription activities have been characterized by a number of sequence-based deep learning approaches which only utilize the DNA sequences. However, transcription factors interact with each other, and their collaborative regulatory activities go beyond the linear DNA sequence. Therefore leveraging the informative 3D chromatin organization to investigate the collaborations among transcription factors is critical. We developed ECHO, a graph-based neural network, to predict chromatin features and characterize the collaboration among them by incorporating 3D chromatin organiz

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Hong, Ziyang, and C. Patrick Yue. "Efficient-Grad: Efficient Training Deep Convolutional Neural Networks on Edge Devices with Grad ient Optimizations." ACM Transactions on Embedded Computing Systems 21, no. 2 (2022): 1–24. http://dx.doi.org/10.1145/3504034.

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With the prospering of mobile devices, the distributed learning approach, enabling model training with decentralized data, has attracted great interest from researchers. However, the lack of training capability for edge devices significantly limits the energy efficiency of distributed learning in real life. This article describes Efficient-Grad, an algorithm-hardware co-design approach for training deep convolutional neural networks, which improves both throughput and energy saving during model training, with negligible validation accuracy loss. The key to Efficient-Grad is its exploitation of

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Troedsson, Christofer, Richard F. Lee, Vivica Stokes, Tina L. Walters, Paolo Simonelli, and Marc E. Frischer. "Development of a Denaturing High-Performance Liquid Chromatography Method for Detection of Protist Parasites of Metazoans." Applied and Environmental Microbiology 74, no. 14 (2008): 4336–45. http://dx.doi.org/10.1128/aem.02131-07.

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ABSTRACT Increasingly, diseases of marine organisms are recognized as significant biotic factors affecting ecosystem health. However, the responsible disease agents are often unknown and the discovery and description of novel parasites most often rely on morphological descriptions made by highly trained specialists. Here, we describe a new approach for parasite discovery, utilizing denaturing high-performance liquid chromatography (DHPLC) reverse-phase ion-paring technology. Systematic investigations of major DHPLC variables, including temperature, gradient conditions, and target amplicon char

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KUMAR, AMRENDER, A. K. MISHRA, A. K. JAIN, and C. CHATTOPADHYAY. "Neural network based prediction models for evaporation." MAUSAM 67, no. 2 (2021): 389–96. http://dx.doi.org/10.54302/mausam.v67i2.1324.

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From statistical perspective, artificial neural networks (ANNs) are interesting because of their potential use in prediction. In this study, ANNs based approach has been used to assess the prediction of evaporation with meteorological variables, viz., maximum temperature (MaxT), minimum temperature (MinT), relative humidity in the morning (RHI), relative humidity in evening (RHII), bright sunshine hours (BSH) and wind speed (WS) for different locations (Una, Karnal, Pantnagar, Raipur, Anantpur, Bangalore and Pattambi) in India. ANNs models were developed using Multilayer perceptron (MLP) archi

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Patgiri, Chayashree, and Amrita Ganguly. "Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method." Asian Pacific Journal of Health Sciences 9, no. 4 (2022): 165–70. http://dx.doi.org/10.21276/apjhs.2022.9.4.33.

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Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously in recent times. Sickle cell anemia (SCA) is a kind of disease connected with red blood cells (RBCs) present in the human body in which deformation of cells take place. The purpose of this work is to propose an automatic image processing technique for the detection of this disease from microscopic blood images. This paper mainly focuses on automatic detection of SCA using a novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For the

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Nawi, Nazri Mohd, Noorhamreeza Abdul Hamid, and Noor Yasmin Zainun. "A New Modified Back-Propagation Algorithm for Forecasting Malaysian Housing Demand." Applied Mechanics and Materials 232 (November 2012): 908–12. http://dx.doi.org/10.4028/www.scientific.net/amm.232.908.

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Over the past decade, the growth of the housing construction in Malaysia has been increase dramatically and the level of urbanization process in Malaysia is considered to be important in planning for low-cost housing needs. Unfortunately, there is a clear miss-match between the supply and the demand of low cost housing in Malaysia. Due to the problems faced, there have been several attempts in predicting housing demands using the artificial-neural networks (ANN) technique particularly back-propagation (BP). However, the training process of BP can result in slow convergence or even network para

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Li, Wenfeng, Kun Pan, Wenrong Liu, et al. "Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion." Agriculture 14, no. 8 (2024): 1265. http://dx.doi.org/10.3390/agriculture14081265.

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Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll content are time-consuming and labor-intensive. The rapid development of UAV remote sensing platforms offers new possibilities for monitoring chlorophyll content in field crops. To improve the efficiency and accuracy of monitoring chlorophyll content in maize canopies, this study collected RGB, multispectral (MS), and SPAD data from maize canopies at the jointing, tasseling, and grouting stages, constructing a dataset with fused features. We deve

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You, Zhang Ping, Xiao Ping Ye, and Wen Hui Zhang. "Hydraulic System Fault Diagnosis Method Based on HPSO AND WP-EE." Applied Mechanics and Materials 577 (July 2014): 438–42. http://dx.doi.org/10.4028/www.scientific.net/amm.577.438.

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A fault diagnosis approach of hydraulic system based on Hybrid Particle Swarm Optimization (HPSO) algorithm and Wavelet Packet Energy Entropy (WP-EE) is presented. A heuristic algorithm is adopted to give a transition from particle swarm search to gradient descending search. A HPSO algorithm is formed with the heuristic algorithm, which is used to optimize BP neural network weights and threshold. Then an application of fault diagnosis with HPSO and WP-EE based on a valve-controlled hydraulic motor system is presented. After wavelet packet decomposition of the acquired hydraulic pressure signal

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Liu, Wenbo, Zhirui Liu, and Hongan Ma. "Exploration of the Ignition Delay Time of RP-3 Fuel Using the Artificial Bee Colony Algorithm in a Machine Learning Framework." Energies 18, no. 12 (2025): 3037. https://doi.org/10.3390/en18123037.

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Ignition delay time (IDT) is a critical parameter for evaluating the autoignition characteristics of aviation fuels. However, its accurate prediction remains challenging due to the complex coupling of temperature, pressure, and compositional factors, resulting in a high-dimensional and nonlinear problem. To address this challenge for the complex aviation kerosene RP-3, this study proposes a multi-stage hybrid optimization framework based on a five-input, one-output BP neural network. The framework—referred to as CGD-ABC-BP—integrates randomized initialization, conjugate gradient descent (CGD),

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Fang, Xi, and Nan Yang. "A Neural Learning Approach for a Data-Driven Nonlinear Error Correction Model." Computational Intelligence and Neuroscience 2023 (January 23, 2023): 1–14. http://dx.doi.org/10.1155/2023/5884314.

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A nonlinear error correction model (ECM) is developed to fit nonlinear relationships between the nonstationary time series in a cointegration relationship. Different from the previous parametric methods, this paper constructs a hybrid neural network to learn the nonlinear error correction model by combining a linear recurrent neural network with a multilayer BP network. The network learning algorithm is given by using the gradient descent method and error back propagation. Based on the principle of data-driven, all network parameters can be obtained through the network learning and training. T

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Milovancevic, Milos, Vlastimir Nikolic, Nenad T. Pavlovic, Aleksandar Veg, and Sanjin Troha. "Vibration prediction of pellet mills power transmission by artificial neural network." Assembly Automation 37, no. 4 (2017): 464–70. http://dx.doi.org/10.1108/aa-06-2016-060.

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Purpose The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring. Design/methodology/approach As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created. Findings Although the achieved prediction results were acceptable, there is need for more work to apply and test these results i

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Xiao, Yi, Yahui Guo, Guodong Yin, et al. "UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China." Remote Sensing 14, no. 14 (2022): 3272. http://dx.doi.org/10.3390/rs14143272.

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Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting

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Billi, Daniela, Maria Grilli Caiola, Luciano Paolozzi, and Patrizia Ghelardini. "A Method for DNA Extraction from the Desert Cyanobacterium Chroococcidiopsis and Its Application to Identification of ftsZ." Applied and Environmental Microbiology 64, no. 10 (1998): 4053–56. http://dx.doi.org/10.1128/aem.64.10.4053-4056.1998.

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ABSTRACT A method was developed for extraction of DNA fromChroococcidiopsis that overcomes obstacles posed by bacterial contamination and the presence of a thick envelope surrounding the cyanobacterial cells. The method is based on the resistance of Chroococcidiopsis to lysozyme and consists of a lysozyme treatment followed by osmotic shock that reduces the bacterial contamination by 3 orders of magnitude. Then DNase treatment is performed to eliminate DNA from the bacterial lysate. Lysis ofChroococcidiopsis cells is achieved by grinding with glass beads in the presence of hot phenol. Extracte

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Habashy, T. M., A. Abubakar, G. Pan, and A. Belani. "Source-receiver compression scheme for full-waveform seismic inversion." GEOPHYSICS 76, no. 4 (2011): R95—R108. http://dx.doi.org/10.1190/1.3590213.

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We have developed a source-receiver compression approach for reducing the computational time and memory usage of the acoustic and elastic full-waveform inversions. By detecting and quantifying the extent of redundancy in the data, we assembled a reduced set of simultaneous sources and receivers that are weighted sums of the physical sources and receivers used in the survey. Because the numbers of these simultaneous sources and receivers could be significantly less than those of the physical sources and receivers, the computational time and memory usage of any gradient-type inversion method suc

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Chen, Syuan-Yi, Cheng-Yen Lee, Chien-Hsun Wu, and Yi-Hsuan Hung. "Intelligent motion control of voice coil motor using PID-based fuzzy neural network with optimized membership function." Engineering Computations 33, no. 8 (2016): 2302–19. http://dx.doi.org/10.1108/ec-08-2015-0250.

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Purpose The purpose of this paper is to develop a proportional-integral-derivative-based fuzzy neural network (PIDFNN) with elitist bacterial foraging optimization (EBFO)-based optimal membership functions (PIDFNN-EBFO) position controller to control the voice coil motor (VCM) for tracking reference trajectory accurately. Design/methodology/approach Because the control characteristics of the VCM are highly nonlinear and time varying, a PIDFNN, which integrates adaptive PID control with fuzzy rules, is proposed to control the mover position of the VCM. Moreover, an EBFO algorithm is further pro

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Satokari, Reetta M., Elaine E. Vaughan, Antoon D. L. Akkermans, Maria Saarela, and Willem M. de Vos. "Bifidobacterial Diversity in Human Feces Detected by Genus-Specific PCR and Denaturing Gradient Gel Electrophoresis." Applied and Environmental Microbiology 67, no. 2 (2001): 504–13. http://dx.doi.org/10.1128/aem.67.2.504-513.2001.

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ABSTRACT We describe the development and validation of a method for the qualitative analysis of complex bifidobacterial communities based on PCR and denaturing gradient gel electrophoresis (DGGE).Bifidobacterium genus-specific primers were used to amplify an approximately 520-bp fragment from the 16S ribosomal DNA (rDNA), and the fragments were separated in a sequence-specific manner in DGGE. PCR products of the same length from different bifidobacterial species showed good separation upon DGGE. DGGE of fecal 16S rDNA amplicons from five adult individuals showed host-specific populations of bi

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Dormoy, I., O. Peyron, N. Combourieu-Neboutb, et al. "Terrestrial climate variability and seasonality changes in the Mediterranean region between 15000 and 4000 years BP deduced from marine pollen records." Climate of the Past Discussions 5, no. 1 (2009): 735–70. http://dx.doi.org/10.5194/cpd-5-735-2009.

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Abstract. Pollen-based climate reconstructions were performed on two high-resolution pollen – marines cores from the Alboran and Aegean Seas in order to unravel the climatic variability in the coastal settings of the Mediterranean region between 15 000 and 4000 cal yrs BP (the Lateglacial, and early to mid-Holocene). The quantitative climate reconstructions for the Alboran and Aegean Sea records focus mainly on the reconstruction of the seasonality changes (temperatures and precipitation), a crucial parameter in the Mediterranean region. This study is based on a multi-method approach comprisin

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Dormoy, I., O. Peyron, N. Combourieu Nebout, et al. "Terrestrial climate variability and seasonality changes in the Mediterranean region between 15 000 and 4000 years BP deduced from marine pollen records." Climate of the Past 5, no. 4 (2009): 615–32. http://dx.doi.org/10.5194/cp-5-615-2009.

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Abstract. Pollen-based climate reconstructions were performed on two high-resolution pollen marines cores from the Alboran and Aegean Seas in order to unravel the climatic variability in the coastal settings of the Mediterranean region between 15 000 and 4000 years BP (the Lateglacial, and early to mid-Holocene). The quantitative climate reconstructions for the Alboran and Aegean Sea records focus mainly on the reconstruction of the seasonality changes (temperatures and precipitation), a crucial parameter in the Mediterranean region. This study is based on a multi-method approach comprising 3

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Farki, Ali, Reza Baradaran Kazemzadeh, and Elham Akhondzadeh Noughabi. "A Novel Clustering-Based Algorithm for Continuous and Noninvasive Cuff-Less Blood Pressure Estimation." Journal of Healthcare Engineering 2022 (January 15, 2022): 1–13. http://dx.doi.org/10.1155/2022/3549238.

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Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, as independent variables and extracting features from arterial blood pressure (ABP) signals as dependent variables and then using machine-learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a nov

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Zhu, Wenjing, Shoufeng Shen, and Zhijun Zhang. "Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets." Computational and Mathematical Methods in Medicine 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/1581958.

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To improve the performance in multiclass classification for small datasets, a new approach for schizophrenic classification is proposed in the present study. Firstly, the Xgboost classifier is introduced to discriminate the two subtypes of schizophrenia from health controls by analyzing the functional magnetic resonance imaging (fMRI) data, while the gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFF) are extracted as the features of classifiers. Then, the D-S combination rule of evidence is used to achieve fusion to determine the basic probability assignment based on t

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Farnleitner, Andreas H., Norbert Kreuzinger, Gerhard G. Kavka, Sonja Grillenberger, Johannes Rath та Robert L. Mach. "Simultaneous Detection and Differentiation ofEscherichia coli Populations from Environmental Freshwaters by Means of Sequence Variations in a Fragment of the β-d-Glucuronidase Gene". Applied and Environmental Microbiology 66, № 4 (2000): 1340–46. http://dx.doi.org/10.1128/aem.66.4.1340-1346.2000.

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ABSTRACT A PCR-based denaturing-gradient gel electrophoresis (DGGE) approach was applied to a partial sequence of the β-d-glucuronidase gene (uidA) for specific detection and differentiation of Escherichia colipopulations according to their uidA sequence variations. Detection of sequence variations by PCR-DGGE and by PCR with direct sequencing correlated perfectly. Screening of 50 E. colifreshwater isolates and reference strains revealed 11 sequence types, showing nine polymorphic sites and an average number of pairwise differences between alleles of the uidA gene fragments (screened fragment

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Huang, Yangke, and Zhiming Wang. "Multi-granularity pruning for deep residual networks." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7403–10. http://dx.doi.org/10.3233/jifs-200771.

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Network pruning has been widely used to reduce the high computational cost of deep convolutional neural networks(CNNs). The dominant pruning methods, channel pruning, removes filters in layers based on their importance or sparsity training. But these methods often give limited acceleration ratio and encounter difficulties when pruning CNNs with skip connections. Block pruning methods take a sequence of consecutive layers (e.g., Conv-BN-ReLu) as a block and remove entire block each time. However, previous methods usually introduce new parameters to help pruning and lead additional parameters an

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Lipsky, Robert H., Chiara M. Mazzanti, Joseph G. Rudolph, et al. "DNA Melting Analysis for Detection of Single Nucleotide Polymorphisms." Clinical Chemistry 47, no. 4 (2001): 635–44. http://dx.doi.org/10.1093/clinchem/47.4.635.

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Abstract Background: Several methods for detection of single nucleotide polymorphisms (SNPs; e.g., denaturing gradient gel electrophoresis and denaturing HPLC) are indirectly based on the principle of differential melting of heteroduplex DNA. We present a method for detecting SNPs that is directly based on this principle. Methods: We used a double-stranded DNA-specific fluorescent dye, SYBR Green I (SYBR) in an efficient system (PE 7700 Sequence Detector) in which DNA melting was controlled and monitored in a 96-well plate format. We measured the decrease in fluorescence intensity that accompa

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Ang, Koon Meng, Cher En Chow, El-Sayed M. El-Kenawy, et al. "A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks." Processes 10, no. 12 (2022): 2579. http://dx.doi.org/10.3390/pr10122579.

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Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optim

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He, Qinglong, and Yanfei Wang. "Reparameterized full-waveform inversion using deep neural networks." GEOPHYSICS 86, no. 1 (2020): V1—V13. http://dx.doi.org/10.1190/geo2019-0382.1.

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Full-waveform inversion (FWI) is a powerful method for providing a high-resolution description of the subsurface. However, the misfit function of the conventional FWI method (metric [Formula: see text]-norm) is usually dominated by spurious local minima owing to its nonlinearity and ill-posedness. In addition, FWI requires intensive wavefield computation to evaluate the gradient and step length. We have considered a general inversion method using a deep neural network (DNN) for the FWI problem. This deep-learning inversion method reparameterizes physical parameters using the weights of a DNN,

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Ding, Wei, Qinghai Xu, and Pavel E. Tarasov. "Examining bias in pollen-based quantitative climate reconstructions induced by human impact on vegetation in China." Climate of the Past 13, no. 9 (2017): 1285–300. http://dx.doi.org/10.5194/cp-13-1285-2017.

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Abstract. Human impact is a well-known confounder in pollen-based quantitative climate reconstructions as most terrestrial ecosystems have been artificially affected to varying degrees. In this paper, we use a human-induced pollen dataset (H-set) and a corresponding natural pollen dataset (N-set) to establish pollen–climate calibration sets for temperate eastern China (TEC). The two calibration sets, taking a weighted averaging partial least squares (WA-PLS) approach, are used to reconstruct past climate variables from a fossil record, which is located at the margin of the East Asian summer mo

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Romstad, Anne, Per Guldberg, Nenad Blau, and Flemming Güttler. "Single-Step Mutation Scanning of the 6-Pyruvoyltetrahydropterin Synthase Gene in Patients with Hyperphenylalaninemia." Clinical Chemistry 45, no. 12 (1999): 2102–8. http://dx.doi.org/10.1093/clinchem/45.12.2102.

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Abstract Background: Deficiency of 6-pyruvoyltetrahydropterin synthase (PTPS) is a recessively inherited disorder that leads to depletion of 5,6,7,8-tetrahydrobiopterin, the obligatory cofactor for hydroxylation of phenylalanine, tyrosine, and tryptophan. A marker for neonatal detection of PTPS deficiency is hyperphenylalaninemia (HPA). Molecular analysis would provide a simple and reliable means for distinguishing PTPS deficiency from other potential causes of HPA. Methods: We developed a method based on PCR in combination with denaturing gradient gel electrophoresis (DGGE) that rapidly scans

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Lai, Kaixuan, Xusheng Wang, and Congjun Cao. "A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections." Sensors 24, no. 9 (2024): 2721. http://dx.doi.org/10.3390/s24092721.

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Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approa

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Zhu, Weiqiang, Kailai Xu, Eric Darve, Biondo Biondi, and Gregory C. Beroza. "Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification." GEOPHYSICS 87, no. 1 (2021): R93—R109. http://dx.doi.org/10.1190/geo2020-0933.1.

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Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity m

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Czymzik, Markus, Rik Tjallingii, Birgit Plessen, et al. "Mid-Holocene reinforcement of North Atlantic atmospheric circulation variability from a western Baltic lake sediment record." Climate of the Past 19, no. 1 (2023): 233–48. http://dx.doi.org/10.5194/cp-19-233-2023.

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Abstract. Knowledge about the timing, amplitude and spatial gradients of Holocene environmental variability in the circum-Baltic region is key to understanding its responses to ongoing climate change. Based on a multi-dating and proxy approach, we reconstruct changes in productivity using total organic carbon (TOC) contents in sediments of Lake Kälksjön (KKJ) from west–central Sweden spanning the last 9612 (+255/-114) years. An exception is the period from 1878 CE until today, in which sedimentation was dominated by anthropogenic lake level lowering and land use. In-lake productivity was highe

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Yang, Chen-Chen, Pi-Yu Shen, Hsin-Yuan Miao, et al. "Enhancing the Photoelectric Properties of Flexible Carbon Nanotube Paper by Plasma Gradient Modification and Gradient Illumination." Processes 12, no. 7 (2024): 1449. http://dx.doi.org/10.3390/pr12071449.

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This study investigates the impact of plasma gradient modification and gradient illumination on the optoelectronic properties of buckypaper (BP), a flexible and large-scale material composed of multi-walled carbon nanotubes (MWCNTs). The BP samples were subjected to argon ion plasma treatment at varying power levels and durations, thereby creating different carrier concentration gradients on the surface. The photovoltage and photocurrent responses of the samples were then measured under uniform full illumination and gradient illumination conditions. The findings revealed that both plasma gradi

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Sokolovsky, A. I., and S. A. Sokolovsky. "On hydrodynamics in the presense of strong external potential field." Journal of Physics and Electronics 29, no. 1 (2021): 21–28. http://dx.doi.org/10.15421/332103.

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On the base of the Boltzmann kinetic equation, hydrodynamics of a dilute gas in the presence of the strong external potential field is investigated. First of all, a gravitational field is meant, because the consistent development of hydrodynamics in this environment is of great practical importance. In the present paper it is assumed that it is possible to neglect the influence of the field on the particle collisions. The study is based on the Chapman–Enskog method in a Bogolyubov’s formulation, which uses the idea of the functional hypothesis. Consideration is limited to steady gas states, wh

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Yao, Zhu Ting, and Hong Xia Pan. "Engine Fault Diagnosis Based on Improved BP Neural Network with Conjugate Gradient." Applied Mechanics and Materials 536-537 (April 2014): 296–99. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.296.

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As a typical reciprocating engine power machinery, complex structure determines its failure brings about the complexity and diversity, it shows the uncertainties of operating environment, system noise and sensor accuracy, and engine fault diagnosis accuracy rate is reduced, taking into account the limitations of traditional BP neural networks, improved BP algorithms include statistical algorithms, additional momentum method, variable learning rate method and conjugate gradient method are studied. Finally, the engine is as an example, engine fault diagnosis experimental system is set, the vibra

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Lu, Li Xin, Yan Zhao, Gui Qin Li, Zheng Li, Xiao Yuan, and Hong Bo Li. "Fault Diagnosis of Bearing Based on Conjugate Gradient BP Algorithm." Advanced Materials Research 1039 (October 2014): 191–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1039.191.

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Largely used in industry field, bearing is one of the most vulnerable components in an equipment. Owing to the complicated and nonlinear relationship between features and corresponding specific fault, it is less efficient to diagnosis the faults in tradition ways ,especially to deal with the fault of a mega machine. BP neural network whose strength is to solve the nonlinear problems makes it more precise and efficient to determine the fault of bearing. Conjugate gradient algorithm is proposed as the training method of the BP neural network. Compared with standard training method, conjugate gra

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Nie, Li Xin, Tian Xia Zhang, Shu Ju Wang, and Li Ping Zhang. "Engine Fault Diagnosis Based on PSO-BP Network." Advanced Materials Research 299-300 (July 2011): 1307–11. http://dx.doi.org/10.4028/www.scientific.net/amr.299-300.1307.

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BP network could deal with some nonlinear problems such as fault diagnosis, but the gradient-based method tends to get stuck in local optima or premature convergence. As a population-based heuristic optimization algorithm, PSO method could increase training accuracy of BP network so as to improve discriminant precision of fault diagnosis of equipment such as engine. Ladder diminishing inertia weights tend to improve network’s convergence precision and efficiency during the late stage of PSO iterations, in addition, LPSO or RegPSO contributes to confining premature convergence. The PSO-BP fault

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Liu, Wei, Jian Jun Cai, and Xi Pin Fan. "A Study of PID Control System Based on BP Neural Network." Advanced Materials Research 328-330 (September 2011): 1908–11. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.1908.

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To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradi

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Yang, Hong Yan, and Xi Jie Zang. "The Prediction of Energy Consumption in Henan Based on Genetic Neural Network." Applied Mechanics and Materials 220-223 (November 2012): 2768–71. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2768.

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In order to avoid the standard BP network shortcomings, the forecast model of energy consumption in henan was constructed based on genetic neural network. Involving the advantages of GA and BP, the algorithm can simultaneously complete genetic selection within a solution space to find the optimal points. Then the BP algorithm searchs the best optimal result from those points by the direction of negative gradient. Simulation indicates the MAPE in GA-BP is 8.45%, lower than that of 19.44% in standard BP. Finally it predicts the total energy consumptions with the 23350 million tons of coal in 201

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Jin, Bao Shi, Yu Hu Zuo, Xiao Dan Ma, Hai Ou Guan, and Feng Tan. "The Application on the Forecast of Plant Disease Based on an Improved BP Neural Network." Advanced Materials Research 433-440 (January 2012): 5469–73. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5469.

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Aiming at the disadvantages of large computing, slow convergence and easily trapping into local minima of traditional BP network, a new method named batch momentum learning algorithm which combining the momentum with batch gradient descent algorithm has been used to be as the learning algorithm of connection weights and threshold of BP neural network, through using this method to forecast the prevalence of plant disease, the convergence speed of BP neural network has been enhanced.

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Kwon, Jungmin, Hyojoon Jin, Henri Calandra, and Changsoo Shin. "Interrelation between Laplace constants and the gradient distortion effect in Laplace-domain waveform inversion." GEOPHYSICS 82, no. 2 (2017): R31—R47. http://dx.doi.org/10.1190/geo2015-0670.1.

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Laplace-domain waveform inversion (WI) is generally used to generate smooth initial velocity models for frequency- or time-domain full-waveform inversion. However, in the inversion results of Laplace-domain WI, anomalies such as salt domes are sometimes shifted. We evaluate the “gradient-distortion effect” that causes undesirable changes in parameter updates and found that this is caused by the relationship between the partial derivatives of Laplace wavefields with respect to two different parameters. By analyzing the gradient of the Laplace-domain misfit function, we found that the gradient d

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Li, Tian Tian, Jian Zhong Shao, Jin Li Zhou, and Tian Zuo Zhang. "The Evaluation of Fabric Prickle Based on BP Neural Network." Advanced Materials Research 441 (January 2012): 645–50. http://dx.doi.org/10.4028/www.scientific.net/amr.441.645.

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A three-layer BP neural network model was established by relating subjective evaluation of fabric prickle level and 16 objective parameters from KES-FB system. The elastic gradient decrease method was adopted for network training to achieve the preset precision of the model which was later applied to fabric prickle level evaluation. Results from this method gave a considerably accuracy compared with actual subjective results which implied a compatibility between BP neural network and traditional subjective evaluation.

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