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Relevant bibliographies by topics / Dynamic weighing method

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Academic literature on the topic 'Dynamic weighing method'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Dynamic weighing method"

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Yin, Rong Sen, and Jia Qiang Yang. "A Dynamic Voltage Compensation Method for Improving Weighing Accuracy." Applied Mechanics and Materials 333-335 (July 2013): 317–21. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.317.

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In order to eliminate the measurement error caused by working voltage fluctuation of weighing sensor, a dynamic voltage compensation method for improving the weighing accuracy was proposed in the paper. The method takes strain gauge type pressure sensor as the weighing sensor and corrects Wheatstone bridge circuit. By synchronously detecting output voltage signal and working voltage of the weighing sensor, the method dynamically compensates the working voltage fluctuation of the weighing sensor, corrects the measurement error caused by the voltage fluctuation of external source and calculates the weight in real time. A weighing system based on the digital signal processor (DSP) was designed. Results of simulation experiment verify the feasibility of the proposed method.

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Wang, Yan Xia, Chun Hui Bao, and Chun Ling Fan. "Research of Dynamic Weighing System Based on Multi-Sensor Data Fusion Technology." Advanced Materials Research 721 (July 2013): 479–82. http://dx.doi.org/10.4028/www.scientific.net/amr.721.479.

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The multi-sensor data fusion techniques is discussed in dynamic weighing system based on the data measured from ultrasonic sensors, speed sensors, capacitive sensors and load cells. This new method can greatly increase the measure precision of the dynamic weighing systems, at the same time it can effectively reduce noise, vibration, electromagnetic interference and other environmental factors on the influence of dynamic weighing system measurement. Judging from the simulation result, this new method proves to be more accurate and stable than ordinary processing methods.

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Jiang, Qing, Xiao Qian Shen, Jin Hui Cai, and Yan Yao. "Application of RBF Neural Network in Dynamic Weighing." Advanced Materials Research 383-390 (November 2011): 1495–99. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1495.

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In order to improve the dynamical respond of the weighing system and to meet the demand of rapid weighing, a new method based on radial basis function neural network (RBFNN) is introduced in this paper. The dynamic system is described as a network and the output values of steady state are predicted by an on-line modeling before the platform has settled to the steady state. The sample weight is calculated according to weighted moving average. The experimental results proved that the neural network method in this paper can be used to effectively reduce the weighing time and to increase the accuracy simultaneously.

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An, Jianqi, Fayang You, Min Wu, and Jinhua She. "Iterative Learning Control for Nonlinear Weighing and Feeding Process." Mathematical Problems in Engineering 2018 (December 20, 2018): 1–9. http://dx.doi.org/10.1155/2018/9425902.

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Due to the nonlinear dynamics in weighing and feeding process, it is difficult to achieve high accuracy with conventional control methods. This paper uses a piecewise linearization method for the nonlinear problem and discusses the application of iterative learning control in weighing and feeding process. First, the nonlinear problem and the repeatability are discussed based on dynamic analysis of weighing and feeding process. Next, a linear state space model is established with a piecewise linearization method. Then, an iterative learning controller is presented by utilizing repetitive characteristics, and the controller parameters are obtained by using a multi-objective optimization method. Finally, simulation results show that the presented control method improves the control performances and the accuracy of feeding.

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Mao, Xiaohui, Liping Fei, Xianping Shang, Jie Chen, and Zhihao Zhao. "A fuzzy evaluation method of road vehicle automatic weighing instrument in dynamic force metrological performance." International Journal of Applied Electromagnetics and Mechanics 64, no. 1-4 (December 10, 2020): 1365–72. http://dx.doi.org/10.3233/jae-209455.

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The measurement performance of road vehicle automatic weighing instrument installed on highways is directly related to the safety of roads and bridges. The fuzzy number indicates that the uncertain quantization problem has obvious advantages. By analyzing the factors affecting the metrological performance of the road vehicle automatic weighing instrument, combined with the fuzzy mathematics theory, the weight evaluation model of the dynamic performance evaluation of the road vehicle automatic weighing instrument is proposed. The factors of measurement performance are summarized and calculated, and the comprehensive evaluation standard of the metering performance of the weighing equipment is obtained, so as to realize the quantifiable analysis and evaluation of the metering performance of the dynamic road vehicle automatic weighing instrument in use, and provide data reference for adopting a more scientific measurement supervision method.

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Zhang, Wei, Chun-li Li, Xiao-feng Di, Mi Chen, and Sheng Tao. "Research on Automotive Dynamic Weighing Method Based on Piezoelectric Sensor." MATEC Web of Conferences 139 (2017): 00203. http://dx.doi.org/10.1051/matecconf/201713900203.

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Paik, J. S., K. W. Lim, and K. B. Lee. "Calibration of Coriolis mass flowmeters using a dynamic weighing method." Flow Measurement and Instrumentation 1, no. 3 (April 1990): 171–75. http://dx.doi.org/10.1016/0955-5986(90)90007-t.

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He, Hui-Mei, Pingjie Huang, Dibo Hou, Wen Cai, Zhe Liu, and Guangxin Zhang. "An Intelligent Signal Processing Method for High-Speed Weighing System." International Journal of Food Engineering 9, no. 2 (May 10, 2013): 179–86. http://dx.doi.org/10.1515/ijfe-2012-0019.

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AbstractThe dynamic weighing signal processing method in high-speed fruits sorting system directly affects the weighing accuracy. This paper describes a basic modeling method for time series, including modeling, order determining, applicability analysis, and prediction. Considering the structural characteristics of weighing system and the limit of PLC sampling speed, a modified auto-regressive (AR) model for stable value prediction of the high-speed weighing signals is presented. Meanwhile, a simulation base on Matlab platform and an implementation example are used to test the method performance. At last, the impacts of environmental changes, sorting speeds, fruits shapes, and sizes on the method performance are discussed. In experimental conditions, this method can be applied to fruits sorting system with sorting speed up to 18 per second for standard fruit (smooth round surface without defects) and an acceptable weighing accuracy can be obtained.

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Feng, Ningning, Xi Kang, Haoyuan Han, Gang Liu, Yan’e Zhang, and Shuli Mei. "Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform." Sensors 20, no. 18 (September 18, 2020): 5363. http://dx.doi.org/10.3390/s20185363.

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Weight is an important indicator of the growth and development of dairy cows. The traditional static weighing methods require considerable human and financial resources, and the existing dynamic weighing algorithms do not consider the influence of the cow motion state on the weight curve. In this paper, a dynamic weighing algorithm for cows based on a support vector machine (SVM) and empirical wavelet transform (EWT) is proposed for classification and analysis. First, the dynamic weight curve is obtained by using a weighing device placed along a cow travel corridor. Next, the data are preprocessed through valid signal acquisition, feature extraction, and normalization, and the results are divided into three active degrees during motion for low, medium, and high grade using the SVM algorithm. Finally, a mean filtering algorithm, the EWT algorithm, and a combined periodic continuation-EWT algorithm are used to obtain the dynamic weight values. Weight data were collected for 910 cows, and the experimental results displayed a classification accuracy of 98.6928%. The three algorithms were used to calculate the dynamic weight values for comparison with real values, and the average error rates were 0.1838%, 0.6724%, and 0.9462%. This method can be widely used at farms and expand the current knowledgebase regarding the dynamic weighing of cows.

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Liu, Qin Xian, Wei Lv, and Wei Bing Bao. "The Application of EMD and Genetic Neural Network Algorithm to the Dynamic Weighing System for Loader." Applied Mechanics and Materials 135-136 (October 2011): 1002–6. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.1002.

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The output signal of pressure sensor installed in the dynamic weighing system for loader contains strong vibration, noise, nonlinear signal. The accuracy of the dynamic weighing system is closely related to the pressure signal. An empirical mode decomposition (EMD) algorithm is proposed to preprocessing the signal contaminated. The real weighing signal is filtered out. a new method based on neural network is used to predicate the nonlinear output. in order to solve the problem that it was easily to sink into the partial minimum , the genetic algorithm was put forward .The emulation analysis and the results show that by using the above method, measure precision within 1% can be obtained.

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Dissertations / Theses on the topic "Dynamic weighing method"

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Aguilera, Mena Jesús Jaime [Verfasser], Thomas [Akademischer Betreuer] Fröhlich, Roman Gutachter] Schwartz, and Tino [Gutachter] [Hausotte. "Dynamic weighing calibration method for liquid flowmeters - A new approach / Jesus Jaime Aguilera Mena ; Gutachter: Roman Schwartz, Tino Hausotte ; Betreuer: Thomas Fröhlich." Ilmenau : TU Ilmenau, 2013. http://d-nb.info/117818417X/34.

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Pikšilingytė, Elita. "Sunkiosios technikos masės nustatymo metodų analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140613_100844-03233.

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Baigiamajame magistro darbe nagrinėjama dinaminio svėrimo įranga A1 Vilnius– Kaunas–Klaipėda kelyje. Išnagrinėtas statinis ir dinaminis svėrimo metodas, naudojama įranga, jos tikslumas ir efektyvumas. Analizuojama dinaminio svėrimo sistema, jos konstrukcija, veikimo principas. Tiriamas įrangos tikslumas. Atliekamas 2012 ir 2013 m. tyrimas pagal Klaipėdos transporto inspekcijos duomenis. Nustatoma, kurios klasės transporto priemonės padarė daugiausia pažeidimų. Nustatoma, kiek transporto priemonių viršijo leistiną apkrovą bei leistinus matmenis. Išnagrinėjus teorinius ir praktinius aspektus, pateikiamos baigiamojo darbo išvados ir pasiūlymai. Darbą sudaro 6 dalys: įvadas, literatūros apžvalga, tiriamoji dalis, eksperimentinis tyrimas, išvados ir siūlymai, literatūros sąrašas. Darbo apimtis – 63 p. teksto be priedų, 50 iliustr., 7 lent., 13 bibliografinių šaltinių. Atskirai pridedami darbo priedai.
The main objective of this master thesis is to analize dynamic weighing system in A1 Vilnius–Kaunas–Klaipėda road. There were analized static and dynamic weighing methods, used equipment, it‘s accuracy and efficiency. Dynamic weighing system ,it‘s construction, accuracy, working principle has been studied. According Klaipėda‘s transport inspection data, 2012 and 2013 years research has been made. Research shows which type of vehicles makes most violations. Determined how many vehicles exceeded permissible load and permissible dimensions. After theoretical and experimental analysis aspects, final conclusions and recommendations were suggested. Structure consists of 6 parts: introduction, literature overview, test part, experimental part, conclusions and suggestions, references. Thesis consist of: 63 p. text without appendixes, 50 pictures, 7 tables, 13 bibliographical entries. Appendixes included.

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Halimic, Mirsad Kjazim. "Performance improvement of dynamic weighing systems using optimal control and advanced signal processing methods." Thesis, Brunel University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249791.

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May, Michael. "Data analytics and methods for improved feature selection and matching." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/data-analytics-and-methods-for-improved-feature-selection-and-matching(965ded10-e3a0-4ed5-8145-2af7a8b5e35d).html.

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This work focuses on analysing and improving feature detection and matching. After creating an initial framework of study, four main areas of work are researched. These areas make up the main chapters within this thesis and focus on using the Scale Invariant Feature Transform (SIFT).The preliminary analysis of the SIFT investigates how this algorithm functions. Included is an analysis of the SIFT feature descriptor space and an investigation into the noise properties of the SIFT. It introduces a novel use of the a contrario methodology and shows the success of this method as a way of discriminating between images which are likely to contain corresponding regions from images which do not. Parameter analysis of the SIFT uses both parameter sweeps and genetic algorithms as an intelligent means of setting the SIFT parameters for different image types utilising a GPGPU implementation of SIFT. The results have demonstrated which parameters are more important when optimising the algorithm and the areas within the parameter space to focus on when tuning the values. A multi-exposure, High Dynamic Range (HDR), fusion features process has been developed where the SIFT image features are matched within high contrast scenes. Bracketed exposure images are analysed and features are extracted and combined from different images to create a set of features which describe a larger dynamic range. They are shown to reduce the effects of noise and artefacts that are introduced when extracting features from HDR images directly and have a superior image matching performance. The final area is the development of a novel, 3D-based, SIFT weighting technique which utilises the 3D data from a pair of stereo images to cluster and class matched SIFT features. Weightings are applied to the matches based on the 3D properties of the features and how they cluster in order to attempt to discriminate between correct and incorrect matches using the a contrario methodology. The results show that the technique provides a method for discriminating between correct and incorrect matches and that the a contrario methodology has potential for future investigation as a method for correct feature match prediction.

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Hakala, Tim. "Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1061.

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A new method of adaptive impulse control is developed to precisely and quickly control the position of machine components subject to friction. Friction dominates the forces affecting fine positioning dynamics. Friction can depend on payload, velocity, step size, path, initial position, temperature, and other variables. Control problems such as steady-state error and limit cycles often arise when applying conventional control techniques to the position control problem. Studies in the last few decades have shown that impulsive control can produce repeatable displacements as small as ten nanometers without limit cycles or steady-state error in machines subject to dry sliding friction. These displacements are achieved through the application of short duration, high intensity pulses. The relationship between pulse duration and displacement is seldom a simple function. The most dependable practical methods for control are self-tuning; they learn from online experience by adapting an internal control parameter until precise position control is achieved. To date, the best known adaptive pulse control methods adapt a single control parameter. While effective, the single parameter methods suffer from sub-optimal settling times and poor parameter convergence. To improve performance while maintaining the capacity for ultimate precision, a new control method referred to as Adaptive Impulse Control (AIC) has been developed. To better fit the nonlinear relationship between pulses and displacements, AIC adaptively tunes a set of parameters. Each parameter affects a different range of displacements. Online updates depend on the residual control error following each pulse, an estimate of pulse sensitivity, and a learning gain. After an update is calculated, it is distributed among the parameters that were used to calculate the most recent pulse. As the stored relationship converges to the actual relationship of the machine, pulses become more accurate and fewer pulses are needed to reach each desired destination. When fewer pulses are needed, settling time improves and efficiency increases. AIC is experimentally compared to conventional PID control and other adaptive pulse control methods on a rotary system with a position measurement resolution of 16000 encoder counts per revolution of the load wheel. The friction in the test system is nonlinear and irregular with a position dependent break-away torque that varies by a factor of more than 1.8 to 1. AIC is shown to improve settling times by as much as a factor of two when compared to other adaptive pulse control methods while maintaining precise control tolerances.

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Chang, Yi-Te, and 張奕得. "A Dynamic Weighting Method and Analysis." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/52745826902692887589.

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碩士
國立交通大學
統計學研究所
104
Markov Chain Monte Carlo method is a universal-used method in numerical integration. In this talk, we will discuss the dynamic weighting MCMC proposed by Wong and Liang (1997), which makes the Markov chain converges faster. In the decades, Metropolis Hasting algorithm is an important simulation method, but there are still some drawbacks in the simulation. For example, the movement of the process can be influenced by some tiny probability nodes. This phenomenon may directly affect to our simulated estimation. Our main work is to review the weighted MCMC and give some theoretical proof in some special cases. Through the manner, we can make the MCMC method more efficient.

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Book chapters on the topic "Dynamic weighing method"

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"Dynamic Weighting." In Advanced Markov Chain Monte Carlo Methods, 165–98. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470669723.ch6.

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A. Cooper, Matthew. "Deterministic Approaches to Transient Trajectory Generation." In Deterministic Artificial Intelligence. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.84476.

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This chapter studies a deterministic approach to transient trajectory generation and control as applied to the forced Van der Pol oscillatory system. This type of system tends towards a strongly nonlinear system, which can be considered chaotic. A classical tuning method, targeted exponential weighting, and isolated trajectory fractionalization trajectory generation methods are examined. Illustrating the given deterministic approach via the Van der Pol system highlights the potentially iterative nature of deterministic methods, and that traditional optimal linear time-invariant control techniques are unable to perform as desired whereas even an idealized nonlinear feedforward control significantly outperforms at the steady-state. It will be shown that utilizing a-priori knowledge of the system dynamics will enable the isolated trajectory fractionalization method to minimize the nonlinear transient effects due to miss-modeled or unmodeled plant dynamics, and that this benefit can be coupled with the targeted exponential weighting approach for greatly decreased trajectory tracking error on the order of a 92% reduction of the objective cost function in the presented case study based on the forced Van der Pol system.

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Benidris, Mohammed A., Salem Elsaiah, and Joydeep Mitra. "Applications of Particle Swarm Optimization in Composite Power System Reliability Evaluation." In Advances in Computational Intelligence and Robotics, 573–610. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8291-7.ch018.

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This chapter introduces a novel technique to evaluate composite power system reliability indices and their sensitivities with respect to the control parameters using a dynamically directed binary Particle Swarm Optimization (PSO) search method. A key point in using PSO in power system reliability evaluation lies in selecting the weighting factors associated with the objective function. In this context, the work presented here proposes a solution method to adjust such weighting factors in a dynamic fashion so that the swarm would always fly on the entire search space rather of being trapped to one corner of the search space. Further, a heuristic technique based on maximum capacity flow of the transmission lines is used in classifying the state space into failure, success, and unclassified subspaces. The failure states in the unclassified subspace can be discovered using binary PSO technique. The effectiveness of the proposed method has been demonstrated on the IEEE RTS.

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Shoji, Isao. "Nonparametric Estimation of Nonlinear Dynamics by Local Linear Approximation." In Chaos and Complexity Theory for Management, 368–79. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2509-9.ch019.

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This chapter discusses nonparametric estimation of nonlinear dynamical system models by a method of metric-based local linear approximation. By specifying a metric such as the standard metric or the square metric on the Euclidean space and a weighting function based on such as the exponential function or the cut-off function, it is possible to estimate values of an unknown vector field from experimental data. It can be shown the local linear fitting with the Gaussian kernel, or the local polynomial modeling of degree one, is included in the class of the proposed method. In addition, conducting simulation studies for estimating random oscillations, the chapter shows the method numerically works well.

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Conference papers on the topic "Dynamic weighing method"

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Zheng, Zhe, Yong Xiao, Hongliang Du, and Xiaoyu Ge. "Application of Dynamic Continuous Weighing Method in Sludge Treatment." In 2013 6th International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2013. http://dx.doi.org/10.1109/icinis.2013.57.

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Dongyun Wang and Kai Wang. "The method of the velocity compensation in dynamic weighing system." In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2010. http://dx.doi.org/10.1109/iske.2010.5680822.

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Wu, Qisheng, Ruoyu Pan, Xianglong Luo, and Lei Li. "A signal processing method for dynamic weighing system by SSA-LVQ network." In Instruments (ICEMI). IEEE, 2009. http://dx.doi.org/10.1109/icemi.2009.5274035.

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Chen, Xiaotian, Hongping Tian, Liming Zou, and Jianping Wang. "A Dynamic Weighing Method for in-Field Eggs Sorting Using Digital Signal Processor (DSP)." In 2018 Detroit, Michigan July 29 - August 1, 2018. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2018. http://dx.doi.org/10.13031/aim.201800757.

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Zhu, Xu, Jianming Guo, and Zhaoqing Xie. "A Dynamic Weighing Method for Portal Crane in Bulk Port: Based on Clustering and BP Neural Network." In 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). IEEE, 2016. http://dx.doi.org/10.1109/iciicii.2016.0017.

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Ding, Yiran, Daolin Zhou, Shimin Yu, Zhenyu Wang, and Gangfeng Tan. "Non-Contact Vehicle Overload Identification Method Based on Body Vibration Theory." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97282.

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Abstract Vehicle overload seriously affects the traffic safety, damages the road infrastructure, and the road service life is reduced. There are many shortcomings in the current detection methods for overloaded vehicles. Traditional static weighing stations are relatively fixed and affect the road traffic efficiency; the cost of weighing in motion station is high, while the precision is not enough; on-board weighing electronic products require the modification of vehicles, which is difficult to promote. In this paper, a non-contact overload detection method based on body vibration is proposed. After the analyzation of the body vibration response of dynamic vehicles under specific vibrational excitation, the load value data can be obtained combined with standard parameters and mathematical calculation model. Firstly, the body vibration response model under specific excitation is established. Then, roadbed facilities are arranged according to specific requirements, cameras calibration are finished, and the identification environment is built. Machine vision technology is used to identify the vibration track of the characteristic point on the vehicle body in the vertical direction. The vibration response characteristic parameters are extracted using the established response model. Finally, the vehicle load value data can be obtained by resolve the characteristic parameters. Compared with the rated load data in the database, the overload judgment of the vehicle is obtained. In the experiment part, the road speed-control hump was used as the vibration excitation source. The vehicle experiments were carried out with Dongfeng Aeolus S30 and Yuejin Shangjun X500. The results show that the load identification error can be controlled within 20%–30%. This method above can detect overload vehicles without affecting the traffic efficiency and also has certain guiding significance for the development of intelligent vehicles.

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Perry, Bradley W., and Homayoon Kazerooni. "Sensing User Input on a Human Exoskeleton Device." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5942.

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To practically help those with limited mobility disorders, human exoskeletons need to be intelligent devices that not only guide the pilot along a particular gait but do so in a manner that is comfortable to the pilot and sensitive to pilot intent. This paper proposes sensing the user’s intent and then weighing gait tracking with amplifying the user’s input. To do so, this method utilizes a strain gauge to sense user intent, a Kalman filter to estimate the user’s input to the system, and an LQ-based controller that will weigh pure amplification of the user’s forces with tracking the ideal gait trajectory. This method is implemented in a simulation that showed the user’s input can be estimated under reasonable noise assumptions. This estimate is then supplied to the LQ-based controller with cost function weights on tracking, amplification, and motor input. Varying these weights changes the motor input torque profile to be smoother for the pilot’s comfort or better for tracking performance. These weights can be used to simply and quickly personalize the exoskeleton’s effect for each pilot. The LQ controller is found to yield a smooth torque profile to the user when compared to a PD trajectory tracking controller.

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Sagdeev, D., Ch Isyanov, I. Gabitov, V. Khairutdinov, M. Farakhov, F. Gumerov, Kh Kharlampidi, R. Khamidullin, and I. Abdulagatov. "TEMPERATURE EFFECT ON DENSITY AND VISCOSITY OF LIGHT, MEDIUM, AND HEAVY CRUDE OILS." In RENEWABLE ENERGY: CHALLENGES AND PROSPECTS. ALEF, 2020. http://dx.doi.org/10.33580/2313-5743-2020-8-1-177-206.

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The density and dynamic viscosity of four light, medium, and heavy (extra-viscous) crude oil samples from Tatarstan Oil Field (Russian Federation) have been measured over the temperature range from (293 to 473) K (for density) and from (293 to 348) K (for viscosity) at atmospheric pressure (101 kPa). The density measurements were made using a new densimeter based on hydrostatic weighing method. The viscosity measurements of the same crude oil samples were made us-ing Brookfield rotational viscometer (DV-II+PRO, LVD-II+PRO). The combined expanded uncertainty of the density, viscosity, atmospheric pressure, and temperature measurements at 0.95 confidence level with a coverage factor of k = 2 is estimated to be 0.16 %, 1.0 %, 1.0 %, and 20 mK, respectively. For validation of the reliability and accuracy of the measured density data and correct operation of the new densimeter, all oil samples were measured using the pycnometric method. The present study showed that the densities measured using the new hydrostatic weighing densimeter (HWD) are agree with the values obtained with pycnometric method within (0.03 to 0.14) %. The measured density and viscosity data were used to develop widerange correlations as a function of temperature and API characteristics. The measured densities were represented using simple function of temperature (polynomial type) with API gravity dependent parameters with an accuracy of AAD within from (0.10 to 0.18) %. The measured viscosity data were also used to develop linear Arrhenius and VTF models. API gravity dependence of the Arrhenius parameters was studied.

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Pery, Marcin. "Dynamic Weighting New method of weighting panels with large numbers of weighting parameters." In 2014 Federated Conference on Computer Science and Information Systems. IEEE, 2014. http://dx.doi.org/10.15439/2014f488.

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Fernandez, Charles, Arun Kr Dev, Rose Norman, Wai Lok Woo, and Shashi Bhushan Kumar. "Dynamic Positioning System: Systematic Weight Assignment for DP Sub-Systems Using Multi-Criteria Evaluation Technique Analytic Hierarchy Process and Validation Using DP-RI Tool With Deep Learning Algorithm." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95485.

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Abstract The Dynamic Positioning (DP) System of a vessel involves complex interactions between a large number of sub-systems. Each sub-system plays a unique role in the continuous overall DP function for safe and reliable operation of the vessel. Rating the significance or assigning weightings to the DP sub-systems in different operating conditions is a complex task that requires input from many stakeholders. The weighting assignment is a critical step in determining the reliability of the DP system during complex marine and offshore operations. Thus, an accurate weighting assignment is crucial as it, in turn, influences the decision-making of the operator concerning the DP system functionality execution. Often DP operators prefer to rely on intuition in assigning the weightings. However, it introduces an inherent uncertainty and level of inconsistency in the decision making. The systematic assignment of weightings requires a clear definition of criteria and objectives and data collection with the DP system operating continuously in different environmental conditions. The sub-systems of the overall DP system are characterized by multi-attributes resulting in a high number of comparisons thereby making weighting distribution complicated. If the weighting distribution was performed by simplifying the attributes, making the decision by excluding part of them or compromising the cognitive efforts, then this could lead to inaccurate decision making. Multi-Criteria Decision Making (MCDM) methods have evolved over several decades and have been used in various applications within the Maritime and Oil and Gas industries. DP, being a complex system, naturally lends itself to the implementation of MCDM techniques to assign weight distribution among its sub-systems. In this paper, the Analytic Hierarchy Process (AHP) methodology is used for weight assignment among the DP sub-systems. An AHP model is effective in obtaining the domain knowledge from numerous experts and representing knowledge-guided indexing. The approach involved examination of several criteria in terms of both quantitative and qualitative variables. A state-of-the-art advisory decision-making tool, Dynamic Positioning Reliability Index (DP-RI), is used to validate the results from AHP. The weighting assignments from AHP are close to the reality and verified using the tool through real-life scenarios.

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