Fast panoramic image stitching algorithm based on parameter regression by Fan GUO, Xiaohu LI, Wentao LIU, Jin TANG

“…In reality, the field of view of images acquired by cameras was usually limited, and the demand for panoramic images was increasing.Therefore, a fast panoramic image stitching algorithm based on parameter regression was proposed for panoramic image sequences.The traditional image registration task was transformed into deep learning combined with machine learning, a multi-scale deep convolutional neural network (MDCNN) based on Gaussian difference pyramid was designed to extract features of stitching images, and LightGBM regression model was used to predict stitching parameters.The transformation matrix and the focal length of the camera were obtained to align the images, and a hyperbolic image fusion algorithm was designed to eliminate the stitching seam between the images.The experimental results show that the proposed algorithm can quickly mosaic images and obtain clearer and more natural panoramic mosaic effects than the existing representative algorithms.It also has good adaptability for infrared images.…”
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[the] Sensorium of Machine Architecture and the Onomatope/o by Bryan Cantley

“…It additionally briefly traces a synthetic structure of a con-structed language to describe the fuzzy parameters of these constructs. …”
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Definition and Measure of the Sound Quality of the Machine by Dariusz PLEBAN

“…The global index of the acoustic quality of the machine, accounting for the relations between the noise level at the workstation and the selected parameters characterising both the machine’s sound activity and the working environment, was adopted as the measure of the sound quality of the machine. …”
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Elliptic Curve Cryptography with Machine Learning by Jihane Jebrane, Akram Chhaybi, Saiida Lazaar, Abderrahmane Nitaj

“…This study includes the generation of optimal parameters for ECC systems using machine learning algorithms.…”
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GRADIENT DESCENT METHOD AND PARAMETER SELECTION FOR IMAGE RESTORATION PROBLEM by Nguyen Dinh Dung

“…Image restoration is often considered one of the data processing steps before the training process for machine learning models is performed. Image restoration problems are often solved by iterative algorithms, where the choice of iteration parameters plays an important role in improving the algorithm's convergence rate. …”
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Identification of Synchronous Generator Electric Parameters Connected to the Distribution Grid by Frolov M. Yu., Fishov A. G.

“…The main feature of the proposed method is that parameter identification is performed while the generator to the grid, so it fits in the technological process of operation of the machine and does not influence on the connection time of the machine. …”
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An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation by Sanyi Li, Hongyu Guan, Peng Liu, Weichao Yue, Qian Wang

“…Stochastic Configuration Networks (SCNs) perform well in machine learning and data mining tasks in complex data environments. …”
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VTOL fixed-wing UAV hybrid system parameter matching by REN Xudong, DENG Tao, DU Tong, DONG Xin, JU Ting

“…Fuel cell UAVs have become a research hotspot in green aviation because of their high efficiency, pollution-free emissions and high energy density. Parameter matching of fuel cell hybrid system has an important impact on the economy, environmental protection and efficiency of UAVs. …”
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Dependency of the pulse dynamic electrochemical machining characteristics of Allvac 718 plus in NaNO3 solution on the machining paraments by Moqi Shen, Shuanglu Duan, Zhengrui Zhou, Zhichun Zhang, Jia Liu, Di Zhu

“…The relationships between these processing parameters and the machining quality of ATI718 Plus were explored by examining the post-processing microstructure, surface roughness, material dissolution rate, and machining precision. …”
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High precision and consistent machining of cylindrical rollers using ceramic lapping plates with both-sides machining method by Tianchen Zhao, Kaiping Feng, Luguang Guo, Binghai Lyu, Weifeng Yao, Junkai Ding, Julong Yuan

“…Results demonstrated that silicon carbide (SiC) ceramics exhibited superior wear resistance and machining performance. Optimal process parameters and their influence weights for SiC plate were identified through orthogonal experiments, revealing that the shape of the lapping plate significantly affects the rollers' shape accuracy. …”
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Modeling of wave-induced drift based on stepwise parameter calibration by Kui Zhu, Xueyao Chen, Lin Mu, Lin Mu, Lin Mu, Dingfeng Yu, Dingfeng Yu, Runze Yu, Zhaolong Sun, Tong Zhou, Tong Zhou

“…This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently sized SAR targets—an offshore fishing vessel (OFV) and a person in the water (PIW)—using parameter stepwise calibration and machine-learning (ML) methods. …”
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Impact of Multiple Operating Parameters Interactions on Load Swing of Tower Cranes by Peijin Liu, Chong Zhao, Yu Sun, Xinhui Zhang

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Application of a Hybrid Model for Data Analysis in Hydroponic Systems by Kuanysh Bakirov, Jamalbek Tussupov, Akhmet Tussupov, Ibraheem Shayea, Aruzhan Shoman

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A Detailed Review for Predicting the Quantity of Sugar From Sugarcane Using Various Models by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

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Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks by Ezgi Kurtulmuş, Ferhat Kurtulmuş, Hayrettin Kuşçu, Bilge Arslan, Ali Osman Demir

Subjects: “…machine learning…”
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Advancements in digital drilling technology for deep engineering: a review by Keqi Fan

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Statistical Validity of Neural-Net Benchmarks by Alain Hadges, Srikar Bellur

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Learning from topology: cosmological parameter estimation from the large-scale structure by Jacky H T Yip, Adam Rouhiainen, Gary Shiu

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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

“…We found a strong correlation (r = 0.93) between the sensitivity of ET estimates to machine-learned parameters and model error (root-mean-square error; RMSE), indicating that reduced sensitivity minimizes error propagation and improves performance. …”
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Influence of Electrode Material and EDM Parameters on Electrode Wear of AISI H13 Steel by Mostafa Adel Abdullah, Nareen Hafidh Obaeed, Aseil Mohammed Radhi, Hiba Adil Ahmed

Subjects: “…Abstract: Electrical discharge machining (EDM) is a non-traditional process that uses the electrical spark discharge to machine electrically conducting materials for geometrically complex shapes or hard materials. …”
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