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Artykuły w czasopismach na temat „Training and calibration”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Glick, Mark, and Gary M. Hieftje. "Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra." Applied Spectroscopy 45, no. 10 (1991): 1706–16. http://dx.doi.org/10.1366/0003702914335238.

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Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with dif

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Smirnov, V. A., and A. B. Snedkov. "Adaptive calibration of the angle sensor for gyroscope." Omsk Scientific Bulletin, no. 177 (2021): 85–88. http://dx.doi.org/10.25206/1813-8225-2021-177-85-88.

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The article proposes methods of control and testing of information-measuring systems. The methods for calibrating the angle sensor included in the flight control system are considered. The principle of creating a test bench for evaluating the accuracy of angle sensor calibration is defined. The process of neural network training is described, which allows to compensate the disadvantages inherent to the traditional calibration method. A comparative analysis of the traditional method of joint calibration of angle sensors with the method based on the use of neural networks is performed

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Robin, Yannick, Johannes Amann, Payman Goodarzi, Tizian Schneider, Andreas Schütze, and Christian Bur. "Deep Learning Based Calibration Time Reduction for MOS Gas Sensors with Transfer Learning." Atmosphere 13, no. 10 (2022): 1614. http://dx.doi.org/10.3390/atmos13101614.

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In this study, methods from the field of deep learning are used to calibrate a metal oxide semiconductor (MOS) gas sensor in a complex environment in order to be able to predict a specific gas concentration. Specifically, we want to tackle the problem of long calibration times and the problem of transferring calibrations between sensors, which is a severe challenge for the widespread use of MOS gas sensor systems. Therefore, this contribution aims to significantly diminish those problems by applying transfer learning from the field of deep learning. Within the field of deep learning, transfer

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Sopianah, Yayah, Kwartarini Murdiastuti, Rosa Amalia, and Lukito Nugroho. "Inter-Examiner Training and Calibration to Assess Dental and Oral Disease in the Elderly." Open Access Macedonian Journal of Medical Sciences 11, no. D (2023): 50–54. http://dx.doi.org/10.3889/oamjms.2023.11499.

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BACKGROUND: Examiner training and calibration in clinical measurement of important variables is carried out to equalize perceptions among examiners. AIM: This study aims to obtain intra- and inter-examiner perception similarities after training and calibration in measuring dental caries, tooth mobility, periodontal pockets, and tooth loss in the elderly. MATERIALS AND METHODS: A total of ten examiners performed clinical measurements of dental caries, tooth mobility, periodontal pockets, and tooth loss. The measurement was carried out by a researcher to examine 25 elderly respondents. The calib

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Fan, Hui-Yuan, Wei-zhen Lu, Guang Xi, and Shang-jin Wang. "An Improved Neural-Network-Based Calibration Method for Aerodynamic Pressure Probes." Journal of Fluids Engineering 125, no. 1 (2003): 113–20. http://dx.doi.org/10.1115/1.1523063.

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Calibration of multihole aerodynamic pressure probe is a compulsory and important step in applying this kind of probe. This paper presents a new neural-network-based method for the calibration of such probe. A new type of evolutionary algorithm, i.e., differential evolution (DE), which is known as one of the most promising novel evolutionary algorithms, is proposed and applied to the training of the neural networks, which is then used to calibrate a multihole probe in the study. Based on the measured probe’s calibration data, a set of multilayered feed-forward neural networks is trained with t

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Semiromi, Majid, Sorush Omidvar, and Bahareh Kamali. "Reducing Computational Costs of Automatic Calibration of Rainfall-Runoff Models: Meta-Models or High-Performance Computers?" Water 10, no. 10 (2018): 1440. http://dx.doi.org/10.3390/w10101440.

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Robust calibration of hydrologic models is critical for simulating water resource components; however, the time-consuming process of calibration sometimes impedes the accurate parameters’ estimation. The present study compares the performance of two approaches applied to overcome the computational costs of automatic calibration of the HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) model constructed for the Tamar basin located in northern Iran. The model is calibrated using the Particle Swarm Optimization (PSO) algorithm. In the first approach, a machine learning algorithm,

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Apostolopoulos, Ioannis D., Silas Androulakis, Panayiotis Kalkavouras, George Fouskas, and Spyros N. Pandis. "Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning." Sensors 24, no. 13 (2024): 4110. http://dx.doi.org/10.3390/s24134110.

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This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algo

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Rajaraman, Sivaramakrishnan, Prasanth Ganesan, and Sameer Antani. "Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks." PLOS ONE 17, no. 1 (2022): e0262838. http://dx.doi.org/10.1371/journal.pone.0262838.

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In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge, therefore biasing the predicted class probabilities toward the majority class. Calibration has been proposed to alleviate some of these effects. However, there is insufficient analysis explaining whether and when calibrating a model would be beneficial. In this study, we perform a systematic analysis of the effect of model calibration on its perf

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Kim, jooah, Yooseok Shin, and Jeong Taeg Seo. "Analysis of Evaluator Reliability for the Raters' Calibration Training." Journal of The Korean Dental Association 58, no. 5 (2020): 284–91. http://dx.doi.org/10.22974/jkda.2020.58.5.002.

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This study analyzed the change in the rater reliability based on the student's practice evaluation process conducted at Yonsei University College of Dentistry. Through this, we suggest the significance of the rater calibration training in the student's practical evaluation of dental college. Nine professors from the department of Conservative Dentistry, Yonsei University College of Dentistry, analyzed the results of class II restoration cases twice in 2017 and once in 2018. Intra Class Correlation (ICC) which is a statistic used to determine the consistency of raters with three or more scores,

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10

Chang, Hui-Ling, Huiling Yuan, and Pay-Liam Lin. "Short-Range (0–12 h) PQPFs from Time-Lagged Multimodel Ensembles Using LAPS." Monthly Weather Review 140, no. 5 (2012): 1496–516. http://dx.doi.org/10.1175/mwr-d-11-00085.1.

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This study pioneers the development of short-range (0–12 h) probabilistic quantitative precipitation forecasts (PQPFs) in Taiwan and aims to produce the PQPFs from time-lagged multimodel ensembles using the Local Analysis and Prediction System (LAPS). By doing so, the critical uncertainties in prediction processes can be captured and conveyed to the users. Since LAPS adopts diabatic data assimilation, it is utilized to mitigate the “spinup” problem and produce more accurate precipitation forecasts during the early prediction stage (0–6 h). The LAPS ensemble prediction system (EPS) has a good s

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Findlay, Chris J., John C. Castura, and Isabelle Lesschaeve. "Feedback calibration: A training method for descriptive panels." Food Quality and Preference 18, no. 2 (2007): 321–28. http://dx.doi.org/10.1016/j.foodqual.2006.02.007.

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Hagedorn, Renate, Thomas M. Hamill, and Jeffrey S. Whitaker. "Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part I: Two-Meter Temperatures." Monthly Weather Review 136, no. 7 (2008): 2608–19. http://dx.doi.org/10.1175/2007mwr2410.1.

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Abstract Recently, the European Centre for Medium-Range Weather Forecasts (ECMWF) produced a reforecast dataset for a 2005 version of their ensemble forecast system. The dataset consisted of 15-member reforecasts conducted for the 20-yr period 1982–2001, with reforecasts computed once weekly from 1 September to 1 December. This dataset was less robust than the daily reforecast dataset produced for the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), but it utilized a much higher-resolution, more recent model. This manuscript considers the calibration of 2-m te

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13

Campos, David, Bin Yang, Tung Kieu, Miao Zhang, Chenjuan Guo, and Christian S. Jensen. "QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models." Proceedings of the VLDB Endowment 17, no. 11 (2024): 2708–21. http://dx.doi.org/10.14778/3681954.3681957.

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We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine learning models, e.g., for classification, on edge devices near sensors such that decisions can be made instantaneously, rather than first having to transmit incoming data to servers. To enable deployment on edge devices with limited storage and computational capabilities, the full-precision parameters in standard models can be quantized to use fewer bits. The resulting quantized models are then calibrated using bac

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Zhou, Zhiyang, Siwei Wei, Xudong Zhang, Wensheng Dou, Muzi Qu, and Yan Cai. "Training Deep Neural Networks with Virtual Smoothing Classes." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 23036–44. https://doi.org/10.1609/aaai.v39i21.34467.

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Learning with softmax cross-entropy on one-hot labels often leads to overconfidence on the correct class. While label smoothing regulates this overconfidence by redistributing some confidence from the correct class to other incorrect classes, it compromises the representation in the logits about the similarity between samples of different classes and may hurt calibration if higher confidence is required for high accuracy. To overcome these limitations, we propose a Virtual Smoothing (VS) label that redistributes certain confidence from the correct class to additional VS classes to regularize o

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Xiong, Zhaomin, Chunlei Wei, Fan Yang, Langfeng Zhu, Rongyong Huang, and Jun Wei. "High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration." Applied Sciences 14, no. 5 (2024): 2105. http://dx.doi.org/10.3390/app14052105.

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This paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inversion and disregard physical oceanography, this study incorporates a bottom-mounted acoustic Doppler current profiler (ADCP) and towed ADCP into LSTM training. Additionally, wind and tidal oceanography data were included as inputs. This study compared high-frequency radar current data before and af

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Kasahara, Ryosuke, Saiko Kino, Shunsuke Soyama, and Yuji Matsuura. "Unsupervised calibration for noninvasive glucose-monitoring devices using mid-infrared spectroscopy." Journal of Innovative Optical Health Sciences 11, no. 06 (2018): 1850038. http://dx.doi.org/10.1142/s1793545818500384.

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Noninvasive, glucose-monitoring technologies using infrared spectroscopy that have been studied typically require a calibration process that involves blood collection, which renders the methods somewhat invasive. We develop a truly noninvasive, glucose-monitoring technique using mid-infrared spectroscopy that does not require blood collection for calibration by applying domain adaptation (DA) using deep neural networks to train a model that associates blood glucose concentration with mid-infrared spectral data without requiring a training dataset labeled with invasive blood sample measurements

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Christensen, Donald W., and Yusuf A. Mehta. "Reference Standards for Use with Indirect Tension Test." Transportation Research Record: Journal of the Transportation Research Board 1630, no. 1 (1998): 37–41. http://dx.doi.org/10.3141/1630-05.

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The purpose of this paper is to describe the fabrication, calibration, and use of high-density polyethylene (HDPE) reference standards for the indirect tension (IDT) creep test, which was developed during the Strategic Highway Research Program and is one of the standard Superpave mixture tests now under further development and implementation. The use of reference standards in conjunction with the IDT test will help to achieve rapid and effective implementation of this promising test method. An IDT reference standard is useful in three ways: as a final step in a thorough calibration of the IDT

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Yao, Pan, Kaifeng Wang, Weiwei Xia, et al. "Effects of Training and Calibration Data on Surface Electromyogram-Based Recognition for Upper Limb Amputees." Sensors 24, no. 3 (2024): 920. http://dx.doi.org/10.3390/s24030920.

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Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration da

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Flanagan, T. P. "Training and staff requirements in measurement, calibration and metrology." Transactions of the Institute of Measurement and Control 9, no. 3 (1987): 133–36. http://dx.doi.org/10.1177/014233128700900305.

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Rakow, Tim, Nigel Harvey, and Sarah Finer. "Improving calibration without training: the role of task information." Applied Cognitive Psychology 17, no. 4 (2003): 419–41. http://dx.doi.org/10.1002/acp.878.

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Edalati, Ali, Alireza Ghaffari, Mahsa Ghazvini Nejad, et al. "OAC: Output-adaptive Calibration for Accurate Post-training Quantization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 16453–61. https://doi.org/10.1609/aaai.v39i16.33807.

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Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy required for their inference. Post-training Quantization (PTQ) techniques have been developed to compress LLMs while avoiding expensive re-training. Most PTQ approaches formulate the quantization error based on a layer-wise Euclidean loss, ignoring the model output. Then, each layer is calibrated using its layer-wise Hessian to update the weights towards minimizing the quantization error. The Hessian is also used fo

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Yu, Meixia, Xiaoping Zheng, and Chuanhui Zhao. "Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network." Electronics 12, no. 22 (2023): 4677. http://dx.doi.org/10.3390/electronics12224677.

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A radial basis function (RBF) neural network-based calibration data prediction model for clock testers is proposed to address the issues of fixed calibration cycles, low efficiency, and waste of electrical energy. This provides a new method for clock tester traceability calibration. First, analyze the mechanism of clock tester calibration parameters and the influencing factors of prediction targets. Based on the learning rules of an RBF neural network, determine the data types of training and testing sets. Second, normalize the training and testing data to avoid the adverse effects of data cha

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Kleider-Tesler, Einat, Anat Prior, and Tami Katzir. "The Role of Calibration of Comprehension in Adolescence: From Theory to Online Training." Journal of Cognitive Education and Psychology 18, no. 2 (2019): 190–211. http://dx.doi.org/10.1891/1945-8959.18.2.190.

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The current study examined the effects of a computerized training program on reading comprehension, confidence ratings, and calibration of comprehension in adolescents with poor and good reading comprehension. Ninety 10th graders participated in the study and completed three training sessions. In each session, participants read two expository texts and answered multiple-choice questions. For each answer they gave, participants also rated their confidence. Participants were assigned to one of three online training conditions that differed in the type of immediate feedback provided after each qu

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Kuvandykov, R. E., and A. A. Chernyshenko. "Modernization of the hardware and software complex of the national standards service in the area of pressure measurement technology for the calibration of vacuum gauges and development of a calibration simulator." Journal of Physics: Conference Series 2059, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/2059/1/012010.

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Abstract The article provides information about the functions and the algorithm of modernization of the software and hardware complex for automating the calibration process of vacuum gauges, offers software as a simulator for learning the procedure for calibrating vacuum gauges and effective training of testers. It is shown that in the department for national standards in the field of pressure measurement of D. I. Mendeleyev Institute for Metrology VNIIM. The modernization makes it possible to reduce the work intensity of the test procedure, to reduce the influence of the "human factor", to in

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Li, Pengfei, Jianyi Yang, and Shaolei Ren. "Expert-Calibrated Learning for Online Optimization with Switching Costs." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 2 (2022): 1–35. http://dx.doi.org/10.1145/3530894.

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We study online convex optimization with switching costs, a practically important but also extremely challenging problem due to the lack of complete offline information. By tapping into the power of machine learning (ML) based optimizers, ML-augmented online algorithms (also referred to as expert calibration in this paper) have been emerging as state of the art, with provable worst-case performance guarantees. Nonetheless, by using the standard practice of training an ML model as a standalone optimizer and plugging it into an ML-augmented algorithm, the average cost performance can be highly u

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Hamill, Thomas M., Renate Hagedorn, and Jeffrey S. Whitaker. "Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation." Monthly Weather Review 136, no. 7 (2008): 2620–32. http://dx.doi.org/10.1175/2007mwr2411.1.

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Abstract As a companion to Part I, which discussed the calibration of probabilistic 2-m temperature forecasts using large training datasets, Part II discusses the calibration of probabilistic forecasts of 12-hourly precipitation amounts. Again, large ensemble reforecast datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Forecast System (GFS) were used for testing and calibration. North American Regional Reanalysis (NARR) 12-hourly precipitation analysis data were used for verification and training. Logistic regression was used to perform the calibration

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Ma, Teng, Fali Li, Peiyang Li, Dezhong Yao, Yangsong Zhang, and Peng Xu. "An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface." Computational and Mathematical Methods in Medicine 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/9476432.

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Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy C-mean clustering

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Wang, Yuxia, Daniel Beck, Timothy Baldwin, and Karin Verspoor. "Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression." Transactions of the Association for Computational Linguistics 10 (2022): 680–96. http://dx.doi.org/10.1162/tacl_a_00483.

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Abstract State-of-the-art classification and regression models are often not well calibrated, and cannot reliably provide uncertainty estimates, limiting their utility in safety-critical applications such as clinical decision-making. While recent work has focused on calibration of classifiers, there is almost no work in NLP on calibration in a regression setting. In this paper, we quantify the calibration of pre- trained language models for text regression, both intrinsically and extrinsically. We further apply uncertainty estimates to augment training data in low-resource domains. Our experim

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Zhao, Xinqiao, Feilong Tang, Xiaoyang Wang, and Jimin Xiao. "SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7525–33. http://dx.doi.org/10.1609/aaai.v38i7.28584.

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Image-level weakly supervised semantic segmentation has received increasing attention due to its low annotation cost. Existing methods mainly rely on Class Activation Mapping (CAM) to obtain pseudo-labels for training semantic segmentation models. In this work, we are the first to demonstrate that long-tailed distribution in training data can cause the CAM calculated through classifier weights over-activated for head classes and under-activated for tail classes due to the shared features among head- and tail- classes. This degrades pseudo-label quality and further influences final semantic seg

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Kamran, Fahad, and Jenna Wiens. "Estimating Calibrated Individualized Survival Curves with Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 240–48. http://dx.doi.org/10.1609/aaai.v35i1.16098.

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In survival analysis, deep learning approaches have been proposed for estimating an individual's probability of survival over some time horizon. Such approaches can capture complex non-linear relationships, without relying on restrictive assumptions regarding the relationship between an individual's characteristics and their underlying survival process. To date, however, these methods have focused primarily on optimizing discriminative performance and have ignored model calibration. Well-calibrated survival curves present realistic and meaningful probabilistic estimates of the true underlying

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Tsilifis, Panagiotis, Piyush Pandita, Sayan Ghosh, and Liping Wang. "Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds." Entropy 24, no. 9 (2022): 1291. http://dx.doi.org/10.3390/e24091291.

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Bayesian techniques for engineering problems, which rely on Gaussian process (GP) regression, are known for their ability to quantify epistemic and aleatory uncertainties and for being data efficient. The mathematical elegance of applying these methods usually comes at a high computational cost when compared to deterministic and empirical Bayesian methods. Furthermore, using these methods becomes practically infeasible in scenarios characterized by a large number of inputs and thousands of training data. The focus of this work is on enhancing Gaussian process based metamodeling and model calib

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Li, Fangyuan, Ting Zheng, and Xuewei Gu. "Prognostic risk factor analysis and nomogram construction for primary liver cancer in elderly patients based on SEER database." BMJ Open 12, no. 10 (2022): e051946. http://dx.doi.org/10.1136/bmjopen-2021-051946.

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ObjectiveTo evaluate the risk factors and construct a nomogram model for the prognosis of primary liver cancer in the elderly based on the data from the US SEER database.MethodsThe latest data of patients with primary liver cancer were extracted from the SEER database using SEER*STAT software, and the required variables were included. The data were screened and then divided into a training cohort and a validation cohort. A nomogram model was constructed by screening the variables through univariate and multivariate Cox analysis. The C-Index, ROC and calibration curves were used for model evalu

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Li, Yuezhe, Yuchou Chang, and Hong Lin. "Cultivating Chan with Calibration." International Journal of Reliable and Quality E-Healthcare 4, no. 4 (2015): 32–51. http://dx.doi.org/10.4018/ijrqeh.2015100102.

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Chan is a superior mental training methodology derived from Buddhism and absorbed the wisdom of religious practitioners, philosophers, and scholars around Eastern Asia through thousands of years. As the primary way of Chan, meditation has clear effects in bringing practitioners' mind into a tranquil state and promoting both the mental and the physical health. The effect of Chan is measurable. The authors propose to establish a Chan science by applying modern experimental sciences to various models. In particular, machine learning methods have been used to classify brain states using electroenc

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Panov, Yury Vladimirovich, Aleksey Nikolaevich Pronnikov, and Krzysztof Lapinski. "The Simulator for the Gas Fuel System – Calibration Process." Applied Mechanics and Materials 838 (June 2016): 63–70. http://dx.doi.org/10.4028/www.scientific.net/amm.838.63.

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The article reviews issues of training process effectiveness increase by means of implementation and adaptation of the simulator of gas injection control system for petrol engine performed by applying LPG TECH Co technologies, when technical training relates to the use of alternative fuels on the motor transport.

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Ilie, Adrian, Kok-Lim Low, Greg Welch, Anselmo Lastra, Henry Fuchs, and Bruce Cairns. "Combining Head-Mounted and Projector-Based Displays for Surgical Training." Presence: Teleoperators and Virtual Environments 13, no. 2 (2004): 128–45. http://dx.doi.org/10.1162/1054746041382375.

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We introduce and present preliminary results for a hybrid display system combining head-mounted and projector-based displays. Our work is motivated by a surgical training application where it is necessary to simultaneously provide both a highfidelity view of a central close-up task (the surgery) and visual awareness of objects and events in the surrounding environment. In this article, we motivate the use of a hybrid display system, discuss previous work, describe a prototype along with methods for geometric calibration, and present results from a controlled human subject experiment. This arti

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Park, Sung-Hwan, Jeseon Yoo, Donghwi Son, Jinah Kim, and Hyung-Sup Jung. "Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network." Remote Sensing 13, no. 20 (2021): 4164. http://dx.doi.org/10.3390/rs13204164.

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Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide

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Gil, Yotam, Shay Elmalem, Harel Haim, Emanuel Marom, and Raja Giryes. "Online Training of Stereo Self-Calibration Using Monocular Depth Estimation." IEEE Transactions on Computational Imaging 7 (2021): 812–23. http://dx.doi.org/10.1109/tci.2021.3098927.

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Partido, Brian B. "Ergonomics Calibration Training Utilizing Photography for Dental Hygiene Faculty Members." Journal of Dental Education 81, no. 10 (2017): 1187–93. http://dx.doi.org/10.21815/jde.017.078.

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Haj-Ali, Reem, and Philip Feil. "Rater Reliability: Short- and Long-Term Effects of Calibration Training." Journal of Dental Education 70, no. 4 (2006): 428–33. http://dx.doi.org/10.1002/j.0022-0337.2006.70.4.tb04097.x.

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Chen, Defang, Jian-Ping Mei, Yuan Zhang, et al. "Cross-Layer Distillation with Semantic Calibration." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7028–36. http://dx.doi.org/10.1609/aaai.v35i8.16865.

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Recently proposed knowledge distillation approaches based on feature-map transfer validate that intermediate layers of a teacher model can serve as effective targets for training a student model to obtain better generalization ability. Existing studies mainly focus on particular representation forms for knowledge transfer between manually specified pairs of teacher-student intermediate layers. However, semantics of intermediate layers may vary in different networks and manual association of layers might lead to negative regularization caused by semantic mismatch between certain teacher-student

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Deng, Juncan, Shuaiting Li, Zeyu Wang, Hong Gu, Kedong Xu, and Kejie Huang. "VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16226–34. https://doi.org/10.1609/aaai.v39i15.33782.

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The Diffusion Transformers Models (DiTs) have transitioned the network architecture from traditional UNets to transformers, demonstrating exceptional capabilities in image generation. Although DiTs have been widely applied to high-definition video generation tasks, their large parameter size hinders inference on edge devices. Vector quantization (VQ) can decompose model weight into a codebook and assignments, allowing extreme weight quantization and significantly reducing memory usage. In this paper, we propose VQ4DiT, a fast post-training vector quantization method for DiTs. We found that tra

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Sulianto, Sulianto, Ernawan Setiono, and Lourina Evanale Orfa. "The Effect of Training Data Series Length on the Performance of the Tank Model for Transforming Rainfall into Runoff Data Series." Environmental Research, Engineering and Management 78, no. 3 (2022): 39–55. http://dx.doi.org/10.5755/j01.erem.78.3.31482.

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The Tank model by Sugawara is included in the lumped model category. As with other types of lumped models, the effectiveness of the application of the Tank model is largely determined by the parameter optimization method applied and the quantity of training data involved in the calibration process. This article proposes the Tank-DE model to transform rain data series into discharge in a watershed. The Tank-DE model is built from a combination of a simulation equation system based on the Tank model and a multi-parameter optimization equation system based on the Differential evolution (DE) Algor

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Kabiri, Shayan, Nick M. Holden, Rory P. Flood, Jonathan N. Turner, and Sharon M. O’Rourke. "X-ray Fluorescence Core Scanning for High-Resolution Geochemical Characterisation of Soils." Soil Systems 8, no. 2 (2024): 56. http://dx.doi.org/10.3390/soilsystems8020056.

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X-ray fluorescence (XRF) core scanners are commonly used for fine-scale geochemical analysis in sediment studies, but data are semi-quantitative and require calibration to convert geochemical element counts to concentrations. Application of XRF core scanning in soil science remains largely untapped. This study employed an ITRAX core scanner to scan grassland soil cores and developed a novel calibration method based on a chemometric approach to characterise soil geochemistry. As soil samples are collected based on depth sampling, this study investigated whether higher resolution element concent

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peng, Wang, Xia Fangchao, Cao Zhongchen, et al. "Intelligent calibration of dual liner CCD intersection testing system based on Backpropagation neural network." Journal of Physics: Conference Series 2891, no. 3 (2024): 032018. https://doi.org/10.1088/1742-6596/2891/3/032018.

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Abstract In order to improve the measurement accuracy and calibration efficiency of the dual liner Charge Couple Device (CCD) intersection measurement system, a calibration technique based on Backpropagation (BP) neural network is proposed. This calibration method does not consider the parameters of the linear CCD camera and treats the entire dual liner CCD intersection measurement system as a whole for calibration. In the testing of the dual liner CCD intersection measurement system, the pixel coordinates of the measured target in the pixel plane of the linear CCD camera will change with the

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Zhang, Jun, Yu Tian, Zongjin Ren, Qingbing Chang, and Zhenyuan Jia. "The calibration of force offset for rocket engine based on deep belief network." Measurement and Control 51, no. 5-6 (2018): 172–81. http://dx.doi.org/10.1177/0020294018776442.

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Background Force offset is an important movement and control parameter in rocket motor development process, and its accurate measurement is a vital guarantee of rocket motor reliable operation, so there is an essential significance to achieve accurate force offset calibration. Methods A novel force offset nonlinear calibration method is proposed based on deep belief network. Experimental platform is established and force offset calibration test is completed. Because the Levenberg -Marquardt process has the advantage of both Newton method and gradient descent method, test data are trained with

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Campos, Valeria, and Luis Luengo. "Dental examiners consistency in applying different oral health indices in permanent dentition for a population-based study." International Journal of Medical and Surgical Sciences 5, no. 3 (2019): 95–99. http://dx.doi.org/10.32457/ijmss.2018.024.

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Different indices to identify dental diseases have been developed. For population-based studies, it is imperative to use simple and reliable indices, but most importantly, clinical examiners should reach an agreement, in order to increase inter-observer reliability and obtain reliable data. The objective of this study was to describe the calibration process of the Dental Aesthetic Index, Community Periodontal Index, Simplified Oral Hygiene Index and International Caries Detection and Assessment System ICDAS II by two academics from the Public Health Department of Dentistry. All calibrations st

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Wang, Chenyu, Xinyu Cheng, Linzhi Jin, et al. "Development and Validation of a Nomogram for Predicting Overall Survival to Concurrent Chemoradiotherapy in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma." BioMed Research International 2022 (July 14, 2022): 1–8. http://dx.doi.org/10.1155/2022/6455555.

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This study aims to develop and validate a effective prognostic nomogram for locally advanced esophageal squamous cell carcinoma (LA-ESCC) patients undergoing concurrent chemoradiotherapy (CCRT). Retrospective analysis of 503 patients with LA-ESCC given CCRT in our hospital from 2009 to 2016 was conducted. Two-thirds of the patients were randomly assigned to the training set ( n = 335 ), and one-third were assigned to the validation set ( n = 168 ). In order to generate the nomogram, multivariate cox regression analysis was undertaken in the training set for uncovering significant prognostic va

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Ionascu, Marian-Emanuel, Nuria Castell, Oana Boncalo, Philipp Schneider, Marius Darie, and Marius Marcu. "Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring." Sensors 21, no. 23 (2021): 7977. http://dx.doi.org/10.3390/s21237977.

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During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that ha

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Kolakowski, Marcin. "Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning." Sensors 21, no. 18 (2021): 6270. http://dx.doi.org/10.3390/s21186270.

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This paper describes an automated method for the calibration of RSS-fingerprinting-based positioning systems. The method assumes using a robotic platform to gather fingerprints in the system environment and using them for training machine learning models. The obtained models are used for positioning purposes during the system operation. The presented calibration method covers all steps of the system calibration, from mapping the system environment using a GraphSLAM based algorithm to training models for radio map calibration. The study analyses four different models: fitting a log-distance pat

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Andrianomena, Sambatra. "Probabilistic learning for pulsar classification." Journal of Cosmology and Astroparticle Physics 2022, no. 10 (2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.

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Abstract In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of class imbalance, the performance of the models, achieving relatively high probability of differentiating the positive class from the negative one (roc-auc ∼ 0.98), is very promising overall. We estimate the predictive entropy of each model predictions and find that DKL is more confident than DGP in its predictions and provides better uncertain

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