Relevant bibliographies by topics / Minimum residual sum of squares

Academic literature on the topic 'Minimum residual sum of squares'

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Published: 8 September 2023

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Journal articles on the topic "Minimum residual sum of squares"

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Zhou, Zhen, L. N. Zhang, Y. Qin, D. Z. Ma, and B. Niu. "Statistical Inference of LZL-Type Mass Flowmeter Life Distribution Model." Key Engineering Materials 458 (December 2010): 173–78. http://dx.doi.org/10.4028/www.scientific.net/kem.458.173.

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Characteristics of field failure data are analyzed in this paper. The failure data and sales record of LZL-type mass flowmeter are used to infer life distribution of this conduct. The lines can be fitted in coordinates of six distribution using least square and the residual sum of squares are compared, the minimum correspond is the best distribution type. The results show that the life distribution style of this conduct is the two parameter exponential distribution, which is the base to analyze and predict failure development, research failure mechanism and draw up maintenance policy.
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DEVITA, HANY, I. KOMANG GDE SUKARSA, and I. PUTU EKA N. KENCANA. "KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS." E-Jurnal Matematika 3, no. 4 (November 28, 2014): 146. http://dx.doi.org/10.24843/mtk.2014.v03.i04.p077.

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Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
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Araveeporn, Autcha. "Comparing Parameter Estimation of Random Coefficient Autoregressive Model by Frequentist Method." Mathematics 8, no. 1 (January 2, 2020): 62. http://dx.doi.org/10.3390/math8010062.

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This paper compares the frequentist method that consisted of the least-squares method and the maximum likelihood method for estimating an unknown parameter on the Random Coefficient Autoregressive (RCA) model. The frequentist methods depend on the likelihood function that draws a conclusion from observed data by emphasizing the frequency or proportion of the data namely least squares and maximum likelihood methods. The method of least squares is often used to estimate the parameter of the frequentist method. The minimum of the sum of squared residuals is found by setting the gradient to zero. The maximum likelihood method carries out the observed data to estimate the parameter of a probability distribution by maximizing a likelihood function under the statistical model, while this estimator is obtained by a differential parameter of the likelihood function. The efficiency of two methods is considered by average mean square error for simulation data, and mean square error for actual data. For simulation data, the data are generated at only the first-order models of the RCA model. The results have shown that the least-squares method performs better than the maximum likelihood. The average mean square error of the least-squares method shows the minimum values in all cases that indicated their performance. Finally, these methods are applied to the actual data. The series of monthly averages of the Stock Exchange of Thailand (SET) index and daily volume of the exchange rate of Baht/Dollar are considered to estimate and forecast based on the RCA model. The result shows that the least-squares method outperforms the maximum likelihood method.
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Liu, Kexin, Weimin Bao, Yufeng Hu, Yiqun Sun, Dongjing Li, Kuang Li, and Lili Liang. "Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting." Water 13, no. 24 (December 7, 2021): 3483. http://dx.doi.org/10.3390/w13243483.

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The ridge estimation-based dynamic system response curve (DSRC-R) method, which is an improvement of the dynamic system response curve (DSRC) method via the ridge estimation method, has illustrated its good robustness. However, the optimization criterion for the ridge coefficient in the DSRC-R method still needs further study. In view of this, a new optimization criterion called the balance and random degree criterion considering the sum of squares of flow errors (BSR) is proposed in this paper according to the properties of model-simulated residuals. In this criterion, two indexes, namely, the random degree of simulated residuals and the balance degree of simulated residuals, are introduced to describe the independence and the zero mean property of simulated residuals, respectively. Therefore, the BSR criterion is constructed by combining the sum of squares of flow errors with the two indexes. The BSR criterion, L-curve criterion and the minimum sum of squares of flow errors (MSSFE) criterion are tested on both synthetic cases and real-data cases. The results show that the BSR criterion is better than the L-curve criterion in minimizing the sum of squares of flow residuals and increasing the ridge coefficient optimization speed. Moreover, the BSR criterion has an advantage over the MSSFE criterion in making the estimated rainfall error more stable.
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Marjetič, Aleš. "Least-squares adjustment taking into account the errors in variables." Geodetski vestnik 65, no. 02 (2021): 205–18. http://dx.doi.org/10.15292/geodetski-vestnik.2021.02.205-218.

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In this article, we discuss the procedure for computing the values of the unknowns under the condition of the minimum sum of squares of the observation residuals (least-squares method), taking into account the errors in the unknowns. Many authors have already presented the problem, especially in the field of regression analysis and computations of transformation parameters. We present an overview of the theoretical foundations of the least-squares method and extensions of this method by considering the errors in unknowns in the model matrix. The method, which can be called ‘the total least-squares method’, is presented in the paper for the case of fitting the regression line to a set of points and for the case of calculating transformation parameters for the transition between the old and the new Slovenian national coordinate systems. With the results based on relevant statistics, we confirm the suitability of the considered method for solving such tasks.
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Pizarro Inostroza, María Gabriela, Francisco Javier Navas González, Vincenzo Landi, Jose Manuel León Jurado, Juan Vicente Delgado Bermejo, Javier Fernández Álvarez, and María del Amparo Martínez Martínez. "Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison." Animals 10, no. 9 (September 18, 2020): 1693. http://dx.doi.org/10.3390/ani10091693.

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SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry.
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UTAMI, NI KETUT TRI, and I. KOMANG GDE SUKARSA. "PENERAPAN METODE GENERALIZED RIDGE REGRESSION DALAM MENGATASI MASALAH MULTIKOLINEARITAS." E-Jurnal Matematika 2, no. 1 (January 30, 2013): 54. http://dx.doi.org/10.24843/mtk.2013.v02.i01.p029.

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Ordinary least square is parameter estimation method for linier regression analysis by minimizing residual sum of square. In the presence of multicollinearity, estimators which are unbiased and have a minimum variance can not be generated. Multicollinearity refers to a situation where regressor variables are highly correlated. Generalized Ridge Regression is an alternative method to deal with multicollinearity problem. In Generalized Ridge Regression, different biasing parameters for each regressor variables were added to the least square equation after transform the data to the space of orthogonal regressors. The analysis showed that Generalized Ridge Regression was satisfactory to overcome multicollinearity.
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Annan, Richard Fiifi, Yao Yevenyo Ziggah, John Ayer, and Christian Amans Odutola. "ACCURACY ASSESSMENT OF HEIGHTS OBTAINED FROM TOTAL STATION AND LEVEL INSTRUMENT USING TOTAL LEAST SQUARES AND ORDINARY LEAST SQUARES METHODS." Geoplanning: Journal of Geomatics and Planning 3, no. 2 (October 25, 2016): 87. http://dx.doi.org/10.14710/geoplanning.3.2.87-92.

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Spirit levelling has been the traditional means of determining Reduced Levels (RL’s) of points by most surveyors. The assertion that the level instrument is the best instrument for determining elevations of points needs to be reviewed; this is because technological advancement is making the total station a very reliable tool for determining reduced levels of points. In order to achieve the objective of this research, reduced levels of stations were determined by a spirit level and a total station instrument. Ordinary Least Squares (OLS) and Total Least Squares (TLS) techniques were then applied to adjust the level network. Unlike OLS which considers errors only in the observation matrix, and adjusts observations in order to make the sum of its residuals minimum, TLS considers errors in both the observation matrix and the data matrix, thereby minimising the errors in both matrices. This was evident from the results obtained in this study such that OLS approximated the adjusted reduced levels, which compromises accuracy, whereas the opposite happened in the TLS adjustment results. Therefore, TLS was preferred to OLS and Analysis of Variance (ANOVA) was performed on the preferred TLS solution and the RL’s from the total station in order to ascertain how accurate the total station can be relative to the spirit level.
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Zverev, S. V., V. I. Karpov, and M. A. Nikitina. "Optimization of food compositions according to the ideal protein profile." Food systems 4, no. 1 (April 28, 2021): 4–11. http://dx.doi.org/10.21323/2618-9771-2021-4-1-4-11.

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The paper emphasizes the importance of not only the quantitative but also qualitative composition of protein in nutrition. The authors propose protein classification into three main groups according to the concept of reference (ideal) protein. A mathematical model is examined to solve the task of rational mixture production upon the given profile of reference protein. Two variants of the criterion for formation of optimal composition are described. One of them presents the classical sum of squares of the residual for essential amino acid scores and 1. The second also presents the sum of squares of the residual for essential amino acid scores and 1 but with regard to only those amino acids, which scores are less than 1. The minima of these criteria at the set of variants for the content of ingredients are taken as targeted functions. The algorithm and the program of calculation were realized in the program environment Builder C++ 6.0. The macro flowchart of the algorithm is presented and detailed description of each block is given. The program interface before and after the start of the calculation module is shown. The main windows and interpretation of the presented data are described. An example of realization of the proposed mathematical apparatus when calculating a food model composition is given. Plant components (white kidney beans, flax, peanut, grit “Poltavskaya», dry red carrot) were used as an object of the research. Most plant proteins were incomplete. It is possible to regulate the chemical composition including correction of a protein profile by combination of plant raw materials. Analysis of alternative variants demonstrated that minimum essential amino acid score in the first composition was 0.79 (by the first criterion), in the second 1.0 (by the second criterion); the reference protein proportion in the mixture was 10.8 and 13.5, respectively, according to the first and second criterion. The comparative results by other quality indicators for protein in the mixture are also presented: the coefficient of amino acid score difference (CAASD), biological value (BV), coefficient of utility, essential amino acids index (IEAA).
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Chiang Hsieh, Lin-Han. "How Does the Effect Fade over Distance? An Inquiry into the Decay Pattern of Distance Effect on Property Values in the Case of Taipei, Taiwan." Land 10, no. 11 (November 12, 2021): 1238. http://dx.doi.org/10.3390/land10111238.

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It is generally accepted that the perception of homeowners towards certain potential risks or amenities fades as distance from the risk or amenity increases. This study aims to illustrate the distance–decay pattern with an appropriate mathematical function. Distance–decay functions and parameters that yield the minimum residual sum of squares (RSS) for a given regression model are considered to be the optimal approximation for the pattern of decay. The effect of flood risk and mass rapid transit (MRT) accessibility on residential housing prices in Taipei, Taiwan, are used as examples to test the optimization process. The results indicate that the type of distance function affects both the significance and the magnitude of the regression coefficients. In the case of Taipei, concave functions provide better fits for both the flood risk and MRT accessibility. RSS reduction is up to 10% compared to the blank. Surprisingly, the impact range for the flood risk is found to be larger than that for MRT accessibility, which suggested that the impact range of perception for uncertain risks is larger than expected.
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Dissertations / Theses on the topic "Minimum residual sum of squares"

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GRIBEL, DANIEL LEMES. "HYBRID GENETIC ALGORITHM FOR THE MINIMUM SUM-OF-SQUARES CLUSTERING PROBLEM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30724@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Clusterização desempenha um papel importante em data mining, sendo útil em muitas áreas que lidam com a análise exploratória de dados, tais como recuperação de informações, extração de documentos e segmentação de imagens. Embora sejam essenciais em aplicações de data mining, a maioria dos algoritmos de clusterização são métodos ad-hoc. Eles carecem de garantias na qualidade da solução, que em muitos casos está relacionada a uma convergência prematura para um mínimo local no espaço de busca. Neste trabalho, abordamos o problema de clusterização a partir da perspectiva de otimização, onde propomos um algoritmo genético híbrido para resolver o problema Minimum Sum-of-Squares Clustering (MSSC, em inglês). A meta-heurística proposta é capaz de escapar de mínimos locais e gerar soluções quase ótimas para o problema MSSC. Os resultados mostram que o método proposto superou os resultados atuais da literatura – em termos de qualidade da solução – para quase todos os conjuntos de instâncias considerados para o problema MSSC.
Clustering plays an important role in data mining, being useful in many fields that deal with exploratory data analysis, such as information retrieval, document extraction, and image segmentation. Although they are essential in data mining applications, most clustering algorithms are adhoc methods. They have a lack of guarantee on the solution quality, which in many cases is related to a premature convergence to a local minimum of the search space. In this research, we address the problem of data clustering from an optimization perspective, where we propose a hybrid genetic algorithm to solve the Minimum Sum-of-Squares Clustering (MSSC) problem. This meta-heuristic is capable of escaping from local minima and generating near-optimal solutions to the MSSC problem. Results show that the proposed method outperformed the best current literature results - in terms of solution quality - for almost all considered sets of benchmark instances for the MSSC objective.
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Snízek, Viktor. "On the Saddlepoint Approximation for the Residual Sum of Squares in Special Linear Heteroscedastic Models." Thesis, Uppsala University, Department of Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121805.

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Masouleh, Mehdi Imani. "Optimal control and stability of four-wheeled vehicles." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:c55c3e9c-270c-4d47-b5f8-e86621608b24.

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Two vehicular optimal control problems are visited. The first relates to the minimum lap time problem, which is of interest in racing and the second the minimum fuel problem, which is of great importance in commercial road vehicles. Historically, minimum lap time problems were considered impractical due to their slow solution times compared with the quasi-steady static (QSS) simulations. However, with increasing computational power and advancement of numerical algorithms, such problems have become an invaluable tool for the racing teams. To keep the solution times reasonable, much attention still has to be paid to the problem formulation. The suspension of a Formula One car is modelled using classical mechanics and a meta-model is proposed to enable its incorporation in the optimal control problem. The interactions between the aerodynamics and the suspension are thereby studied and various related parameters are optimised. Aerodynamics plays a crucial role in the performance of Formula One cars. The influence of a locally applied perturbation to the aerodynamic balance is investigated to determine if a compromise made in design can actually lead to lap time improvements. Various issues related to minimum lap time calculations are then discussed. With the danger of climate change and the pressing need to reduce emissions, improvements in fuel consumption are presently needed more than ever. A methodology is developed for fuel performance optimisation of a hybrid vehicle equipped with an undersized engine, battery and a flywheel. Rather than using the widely used driving cycles, a three-dimensional route is chosen and the optimal driving and power management strategy is found with respect to a time of arrival constraint. The benefits of a multi-storage configuration are thereby demonstrated. Finally, the nonlinear stability of a vehicle model described by rational vector fields is investigated using region of attraction (RoA) analysis. With the aid of sum-of-squares programming techniques, Lyapunov functions are found whose level sets act as an under-approximation to the RoA. The influence of different vehicle parameters and driving conditions on the RoA is studied.
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Mun, Jungwon. "Diagnostics for repeated measurements using residual sum of squares : TRSS plot and its application /." 2006. http://www.library.wisc.edu/databases/connect/dissertations.html.

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Book chapters on the topic "Minimum residual sum of squares"

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Dao, Thi-Bich-Hanh, Khanh-Chuong Duong, and Christel Vrain. "Constrained Minimum Sum of Squares Clustering by Constraint Programming." In Lecture Notes in Computer Science, 557–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23219-5_39.

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Hoai An, Le Thi, and Pham Dinh Tao. "Minimum Sum-of-Squares Clustering by DC Programming and DCA." In Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, 327–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04020-7_35.

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Merz, Peter. "An Iterated Local Search Approach for Minimum Sum-of-Squares Clustering." In Advances in Intelligent Data Analysis V, 286–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45231-7_27.

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Liu, Yongguo, Libin Wang, and Kefei Chen. "A Tabu Search Based Method for Minimum Sum of Squares Clustering." In Pattern Recognition and Data Mining, 248–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_27.

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Minh, Le Hoai, Le Thi Hoai An, and Pham Dinh Tao. "Gaussian Kernel Minimum Sum-of-Squares Clustering and Solution Method Based on DCA." In Intelligent Information and Database Systems, 331–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28490-8_35.

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Pereira, Thiago, Daniel Aloise, Jack Brimberg, and Nenad Mladenović. "Review of Basic Local Searches for Solving the Minimum Sum-of-Squares Clustering Problem." In Open Problems in Optimization and Data Analysis, 249–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99142-9_13.

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Hoai Minh, Le, and Ta Minh Thuy. "DC Programming and DCA for Solving Minimum Sum-of-Squares Clustering Using Weighted Dissimilarity Measures." In Transactions on Computational Intelligence XIII, 113–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54455-2_5.

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Haouas, Mohammed Najib, Daniel Aloise, and Gilles Pesant. "An Exact CP Approach for the Cardinality-Constrained Euclidean Minimum Sum-of-Squares Clustering Problem." In Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 256–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58942-4_17.

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Zhou, Yu, and Zepeng Zhuo. "RETRACTED CHAPTER: On the Minimum the Sum-of-Squares Indicator of a Balanced Boolean Function." In Communications and Networking, 314–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66628-0_30.

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Zhou, Yu, and Zepeng Zhuo. "Retraction Note to: On the Minimum the Sum-of-Squares Indicator of a Balanced Boolean Function." In Communications and Networking, C1. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-66628-0_57.

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Conference papers on the topic "Minimum residual sum of squares"

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Rayces, J. L., and Lan Lebich. "Hybrid method of lens optimization." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.mf4.

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In the typical lens optimization problem there are two groups of equations. The first group which includes optomechanical constraints must be solved exactly, while the second group which includes aberrations in general admits an approximate solution. In Spencer’s method the sum of the squares of the residuals of the latter group is reduced to a minimum. If all the equations in the second group except one are eliminated, and that equation represents the norm of the vector of parameter changes, the system is a solution of Glatzel’s method. This happens automatically when the damping factor added to the diagonal in Spencer’s matrix approaches infinity. There is, therefore, a gradual transition from Spencer’s solution to Glatzel’s solution, and it is possible to combine both methods into one.
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Cho, Hyuk, Inderjit S. Dhillon, Yuqiang Guan, and Suvrit Sra. "Minimum Sum-Squared Residue Co-clustering of Gene Expression Data." In Proceedings of the 2004 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2004. http://dx.doi.org/10.1137/1.9781611972740.11.

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Yao, Qiang, Xiangjing Lai, and Zhetong Zhu. "A memetic algorithm for the balanced minimum sum-of-squares clustering problem." In 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9727258.

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Nesbitt, Richard T., Sudhakar M. Pandit, and Christian M. Muehlfeld. "Powersplit Hybrid Electric Vehicle Control With Data Dependent Systems Forecasting." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42260.

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The focus of this paper is on the implementation of Data Dependent Systems (DDS) forecasting in to the control algorithm of the 2001 Michigan Tech Future Truck. The 2001 MTU Future Truck is a 2000 model year Chevrolet Suburban and utilizes a powersplit transmission, which is similar to the Toyota Prius, for its hybrid conversion. The main source of propulsion comes from a General Motors, all aluminum block, 3.5L V-6. In the Future Truck, the accessory current is not directly measured, so it must be calculated from the measured motor current, generator current and battery current. Accessory current is defined as the current used by all of the high voltage components such as the power steering and AC compressor, except the primary drive motor. In order for the vehicle to be charge sustaining, the generator must produce the same amount of power consumed by the accessories and the drive motor. This calculation will only indicate what the accessory load was at the previous sample time and not what the accessory load will be at the current sample time. When it comes to control of the vehicle, this creates a lag, and the controls will undershoot or overshoot the desired accessory current, which creates inefficiencies due to excessive power flow into and out of the battery pack. In order to better understand the accessory load, Data Dependent Systems (DDS) modeling was done on accessory current data collected from the test vehicle, and an AR(26) model was concluded to be adequate, based on the residual sum of squares (RSS) and unified auto-correlations. The DDS modeling of the accessory current also led to the forecasting of the accessory loads. This helps keep battery use to a minimum by allowing the generator to create the correct amount of power, at that time step, to operate the accessories. Accessory draw from the batteries and generator overshoot going into the batteries is minimized and therefore the overall efficiency of the vehicle goes up. The vehicle was tested on a 50-mile circuit including city and highway driving and elevation changes. The results from the test vehicle showed a power savings of 892 kJ/hour which improved the fuel economy by 3 mpg over stock. The charge sustainability of the vehicle was also achieved which means the range of the vehicle is only limited by the fuel mileage, similar to a conventional vehicle.
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Firdiansyah, Adin Lazuardy, Nur Shofianah, and Marjono. "The Least-Squares Finite Element and Minimum Residual Method for Linear Hyperbolic Problems." In International Conference on Mathematics and Islam. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0008520702780283.

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Sun, Qin, Xiangjing Lai, and Qiang Yao. "An iterated variable neighborhood search algorithm for the balanced minimum sum-of-squares clustering problem." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9327415.

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Pai, M. C., and A. Sinha. "Generating Command Inputs to Eliminate Residual Vibration via Direct Optimization and Quadratic Programming Techniques." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0918.

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Abstract This paper deals with the construction of optimal command inputs to eliminate residual vibration in a flexible structure with single and multiple modes as well. First, the time-optimal command inputs are generated by direct parametric optimization with Lagrangian multipliers. Next, the command input is generated in the form of a sequence of step commands via the method of quadratic programming in which the sum of squares of step commands is minimized. The results from numerical simulations are presented.
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Dias, Fernando G., Jose V. C. Vargas, Sam Yang, Vanessa M. Kava, Wellington Balmant, Andre B. Mariano, and Juan C. Ordonez. "Experimental Calibration of a Biohydrogen Production Estimation Model." In ASME 2018 Verification and Validation Symposium. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/vvs2018-9341.

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In this work, a dynamic physics-based model developed for the prediction of biohydrogen production in a compact tubular photobioreactor was calibrated experimentally. The spatial domain in the model was discretized with lumped control volumes, and the principles of classical thermodynamics, mass, species and heat transfer were combined to derive a system of ordinary differential equations whose solution was the temperature and mass fraction distributions across the entire system. Two microalgae species, namely, Acutodesmus obliquus and Chlamydomonas reinhardtii strain ccI25 were cultured in triplicate with different culture media via indirect biophotolysis. Experimental biomass and hydrogen concentrations were then used to adjust the specific microalgae growth and hydrogen production coefficients based on residual sum of squares and the direct search method.
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Turek, Steven, and Sam Anand. "A Hull Normal Approach for Determining the Size of Cylindrical Features." In ASME 2007 International Manufacturing Science and Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/msec2007-31206.

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In coordinate metrology, discrete data is sampled from a continuous form to assess the manufactured feature’s deviation from its design specifications. Although coordinate measuring machines have a high degree of accuracy, the unsampled portion of the manufactured object cannot be completely described. The definition of cylindrical size for an external feature as specified by ASME Y14.5.1M-1994 [1,2] matches the analytical definition of a minimum circumscribing cylinder (MCC) when Rule #1 is applied. Even though the MCC is a logical analysis technique for size determination, it is highly sensitive to the sampling method and any uncertainties encountered in that process. Determining the least-sum-of-squares solution is an alternative method commonly utilized in size determination. However, the least-squares formulation seeks an optimal solution not based on the cylindrical size definition [1,2], and hence has been shown to be biased [6,7]. This research presents a novel Hull Normal method for size determination of cylindrical bosses. The goal of the proposed method is to recreate the sampled surface using computational geometry methods and determine the cylinder’s axis and radius based upon the reconstructed surface. Through varying the random sample size of data from an actual measured part, repetitive analyses resulted in the Hull Normal method having a lower bias and distributions that were skewed towards the true value of the radius.
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10

Juluri, Naresh, Elie Dib, Sherif el-Gebaly, and Philip Cooper. "Reliability Based Deep Water Spool Piece Design." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10010.

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Abstract:
Long spools are often required to absorb the end expansion of deep water high pressure and high temperature flowlines. These spools typically have significant metrology and fabrication tolerances. Metrology and spool fabrication tolerances lead to misalignments at the connector hub face. Residual loads then arise from spool deformation due to the installation forces that are required to match-up the connector faces. It is a current industry practice to design the spools for multiple independent tolerances at extreme limits in all directions. Previous project experience shows that the Algebraic Sum (AS) combination of multiple independent tolerances at extreme limits may result in large spools where the probability of occurrence of these tolerances at extreme limits is quite low. The use of less conservative SRSS (square root of sum of squares) combination has been suggested in this paper as an alternative to the Algebraic Sum combination. Due to the large number of misalignment components, the probability of exceeding the loads in the spool and at the connector obtained by the SRSS method is small and is within the applicable failure probabilities defined in DNV-OS-F101. The SRSS method is demonstrated in this paper by using a Monte Carlo simulation. Five different spools have been analysed to demonstrate the suitability of using SRSS misalignments when the spools are designed to DNV-OS-F101. The spools considered include 10″, 16″ and 20″ outside diameter spools to represent different sizes at different loading combinations. Maximum bending moments in the spool and maximum moments at the connector have been considered to check the SRSS feasibility. The results indicate that it is acceptable to use SRSS misalignments as an alternative to AS misalignments. Considering SRSS misalignments in preference to AS leads to reduced spool size and reduced loadings on connectors.
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