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Relevant bibliographies by topics / Training and calibration

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Academic literature on the topic 'Training and calibration'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Training and calibration"

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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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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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Dissertations / Theses on the topic "Training and calibration"

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Gao, Xiang Hui. "Development of an initial-training-free online extreme learning machine with applications to automotive engine calibration and control." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691047.

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Wilson, Jamie Graham. "An investigation into GPS brand reporting differences and validation of a between-brand calibration tool for football." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/210148/1/Jamie_Wilson_Thesis.pdf.

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The number of GPS devices brands, commercially available to football teams has risen significantly in the past two decades. As players continue to change between teams such as club, country, youth, professional, their activity may be tracked with two or more brands. Discrepancies between device brand reporting can affect the decisions to adjust individual training load and place players at a higher risk of injury when multiple brands are being used. This project of research has investigated discrepancies between commercial GPS brands and provided a correction tool to be used when comparing GPS

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Macias, Michael S. "The Development of an Optimized System of Narcotic and Explosive Contraband Mimics for Calibration and Training of Biological Detectors." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/123.

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Current commercially available mimics contain varying amounts of either the actual explosive/drug or the chemical compound of suspected interest by biological detectors. As a result, there is significant interest in determining the dominant chemical odor signatures of the mimics, often referred to as pseudos, particularly when compared to the genuine contraband material. This dissertation discusses results obtained from the analysis of drug and explosive headspace related to the odor profiles as recognized by trained detection canines. Analysis was performed through the use of headspace solid

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Scotese, Kyle C. "A Diatom Phosphorus Inference Model for 30 Freshwater Lakes in NE Ohio and NW Pennsylvania." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1231522511.

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Books on the topic "Training and calibration"

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Division, Montana Environmental Management. Equipment pesticide training manual. State of Montana, Dept. of Agriculture, Environmental Management Division, 1986.

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Butcher, Jay L. Calibration Training Program. Independent Publisher, 2012.

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Butcher, Jay L. Calibration Training Program. Independent Publisher, 2012.

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Corporation, Industrial Training. Instrument Calibration (Student Workbook) (Instrument Technology Training Program / Instrumentation Basics, Volume 15). Produced by Industrial Training Corporation for Instrument Society of America, 1992.

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Tkaczyk, Viktoria, Mara Mills, and Alexandra Hui, eds. Testing Hearing. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197511121.001.0001.

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Testing Hearing: The Making of Modern Aurality argues that the modern cultural practices of hearing and testing have emerged from a long interrelationship. Since the early nineteenth century, auditory test tools (whether organ pipes or electronic tone generators) and the results of hearing tests have fed back into instrument calibration, human training, architecture, and the creation of new musical sounds. Hearing tests received a further boost around 1900 as a result of injury compensation laws and state and professional demands for aptitude testing in schools, conservatories, the military, a

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Book chapters on the topic "Training and calibration"

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Da, Qingan, Guoyin Zhang, Wenshan Wang, and Ye Tian. "Adversarial Training Based on Latent Distribution Calibration." In Computational and Experimental Simulations in Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-44947-5_35.

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Leelar, Bhawani Shankar, and Meelis Kull. "Generality-Training of a Classifier for Improved Calibration in Unseen Contexts." In Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43424-2_23.

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AbstractArtificial neural networks tend to output class probabilities that are miscalibrated, i.e., their reported uncertainty is not a very good indicator of how much we should trust the model. Consequently, methods have been developed to improve the model’s predictive uncertainty, both during training and post-hoc. Even if the model is calibrated on the domain used in training, it typically becomes over-confident when applied on slightly different target domains, e.g. due to perturbations or shifts in the data. The model can be recalibrated for a fixed list of target domains, but its perform

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Ott, Simon, Melisachew Wudage Chekol, Christian Meilicke, and Heiner Stuckenschmidt. "Training-Free Score Calibration for Complex Query Decomposition." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94575-5_11.

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Guattari, M. Claudia, Maria Rosaria De Blasiis, Alessandro Calvi, and Andrea Benedetto. "Calibration of an Eye-tracking System for Variable Message Signs Validation." In Driver Behaviour and Training: Volume 4. Routledge, 2024. http://dx.doi.org/10.4324/9781003577973-29.

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Maroñas, Juan, Daniel Ramos, and Roberto Paredes. "On Calibration of Mixup Training for Deep Neural Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73973-7_7.

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Heinemann, Patrick, Frank Sehnke, Felix Streichert, and Andreas Zell. "Towards a Calibration-Free Robot: The ACT Algorithm for Automatic Online Color Training." In RoboCup 2006: Robot Soccer World Cup X. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74024-7_34.

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Yi, Jordan Low Jun, Kuai En Kai Ethan, Raghuveer Singh, and Aung Phyo Phyo Wai. "Virtual Arm with Multimodal Biased Feedback for Improving EEG Motor Imagery Calibration Training." In IRC-SET 2022. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7222-5_1.

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Conforto, S., S. A. Sciuto, D. Bibbo, and A. Scorza. "Calibration of a measurement system for the evaluation of efficiency indexes in bicycle training." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_27.

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Li, Hansheng, Xin Han, Yuxin Kang, et al. "A Novel Loss Calibration Strategy for Object Detection Networks Training on Sparsely Annotated Pathological Datasets." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59722-1_31.

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Tang, Siao, Xin Wang, Hong Chen, et al. "Post-training Quantization with Progressive Calibration and Activation Relaxing for Text-to-Image Diffusion Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72992-8_23.

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Conference papers on the topic "Training and calibration"

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Wang, Yancong, Ze Tao, and Jian Zhang. "Multi-Level Feature Calibration for Adversarial Training Against Attack." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00121.

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Ghaffari, Alireza, Sharareh Younesian, Boxing Chen, Vahid Nia, and Masoud Asgharian. "Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013348800003905.

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Luo, Zhaotong, Guohang Yan, Xinyu Cai, and Botian Shi. "Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything Model." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610983.

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Wang, Yanwei, Wei Huang, Le Yang, et al. "Efficient calibration dataset sampling for post-training quantization of diffusion models." In 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), edited by Dehai Zhang and Tao Lei. SPIE, 2024. http://dx.doi.org/10.1117/12.3037447.

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Williams, Miles, and Nikolaos Aletras. "On the Impact of Calibration Data in Post-training Quantization and Pruning." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.544.

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Sachdeva, Rachneet, Martin Tutek, and Iryna Gurevych. "CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration." In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.eacl-long.113.

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Penso, Coby, and Jacob Goldberger. "A Joint Training and Confidence Calibration Procedure That is Robust to Label Noise." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635160.

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Gong, Biao, Siteng Huang, Yutong Feng, Shiwei Zhang, Yuyuan Li, and Yu Liu. "Check, Locate, Rectify: A Training-Free Layout Calibration System for Text- to- Image Generation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00633.

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Zheng, Yan, Yanlong Zhai, Yanglin Liu, and You Li. "Fractal Augmented Pre-training and Gaussian Virtual Feature Calibration for Tackling Data Heterogeneity in Federated Learning." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650808.

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Wang, Jingfei, Jinliang Wang, Wang Yuan, et al. "A Simulation Training System for Electric Energy Meter Calibration Based on the Combination of Virtual and Real." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796716.

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Reports on the topic "Training and calibration"

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Thulasidasan, Sunil, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, and Sarah E. Michalak. On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1525811.

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Wetzel, Pual A., and Gretchen Anderson. Portable Eye-Tracking System Used During F-16 Simulator Training Missions at Luke AFB: Adjustment and Calibration Procedures. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada368304.

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are ob

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Karstensen, Johannes, Alexandra Andrae, Ludwig Bitzan, et al. Student cruise: Observing techniques for Physical Oceanographers Cruise No. AL529. GEOMAR, 2020. http://dx.doi.org/10.3289/cr_al529.

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Oct. 07 2019 – Oct. 10, 2019 Kiel (Germany) – Kiel (Germany) MNF-Pher-110The main purpose of the ALKOR cruise AL529 was the training of students in observational techniques applied by physical oceanographers. The students who participated in the trip attend the module "Measurement Methods of Oceanography" which is offered in the Bachelor program "Physics of the Earth System" at CAU Kiel. During the AL529 the students were instructed in instrument calibration and in the interpretation of measurement data at sea. In addition, the students had the opportunity to learn about working and living at

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Mizrach, Amos, Sydney L. Spahr, Ephraim Maltz, et al. Ultrasonic Body Condition Measurements for Computerized Dairy Management Systems. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568109.bard.

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The body condition (BC) score is recognized in the dairy industry as an essential tool for managing the energy reserves of the dairy cow, which is essential for sustaining optimal and efficient production over several lactations. The current use of BC scoring depends on the accuracy of subjective visual estimates, and this limits its kusefulness as a management aid in the dairy industry. A measuring tool that would frequently provide objective data on the cow's body reserves would be a major contribution to efficient dairy herd management. Ultrasonic sensors have the potential to be developed

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Madhur, Srinivasa. Cambodia’s Skill Gap: An Anatomy of Issues and Policy Options. Cambodia Development Resource Institute, 2014. https://doi.org/10.64202/wp.98.201408.

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There is growing consensus that an emerging skill gap could impose human costs and constraints on Cambodia’s economic growth and development. The country is facing a shortage of skilled human resources even for low-to-medium skill intensive industries. There is a widening gap between the skills that industries and businesses need and what the education institutions, whether academic or vocational training, are producing. Cambodia’s skill gap is emerging at a time when the Association of Southeast Asian Nations (ASEAN) is preparing to launch the ASEAN Economic Community (AEC) in 2015. The AEC w

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