BULETINUL INSTITUTULUI POLITEHNIC DIN IAŞI

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48 Petronela Paraschiv interpolate functions φ( x) have continuous derivates until a random order (Elias et al., 2004; Tannous et al., 1996; Wagenaar & von Emmerik, 1996). The interpolation of a discreet set of data [x, y] supposes the determination of a function f(x) so that f(x) = y, in order to complete the set of data in any other point x 0 ≠ x 1 . The function f(x) represents the best approximation of the set of data, in this case not being necessary for the determined function to pass through all the given points, but it has to be “the best approximation” after a certain error criterion imposed. Therefore, the method of the smallest squares gives, for instance, the best approximation in the sense of minimizing the squre distances from the given points and the approximation points. 3. Conclusions 1. Applying an interpolation function, linear, spline cubic or polynomial, for the whole set of data or only for some periods of the values, determines the interpolation function coefficients so that the interpolation curve to pass through all given value points. 2. The cubic spline curve is an even curve, defined by a set of polynomials of the third degree. The curve between each pair of points is a polynomial of the third degree calculated in such a way as to lead to even transitions from a polynomial of the third degree to the other. 3. For the situation in which two curves need to be compared, one experimental given by relative values, and one theoretical given by absolute values, the interpolation may allow finding a convenient function of interpolation, the same for both curves to be compared, in which the mathematic function coefficients differ from ne curve to the other. 4. The interpolation function found needs to satisfy the condition that it represents the best approximation for any other two curves, experimental and theoretical, that express the variation of the same physical phenomenon. REFERENCES Antonescu D., Buga M., Constantinescu I., Iliescu M., Metode de calcul şi tehnici experimentale de analiza tensiunilor în biomecanică. Ed. Tehnică, Bucureşti, 1986. Azar F. M., Calandruccio J. H., Arthroplasty of the Shoulder and Elbow. (Canale, S. T., Beaty, J. H., Eds.) Campbell’s Operative Orthopaedics, 11th Ed. Mosby, Phyladelphia, 2008. Elias J.J., Wilson D.R., Adamson R., Cosgarea A.J., Evaluation of a Computational Model Used to Predict the Patellofemoral Contact Pressure Distribution. Journal of Biomechanics, 37(3), 295-302 (2004).
Bul. Inst. Polit. Iaşi, t. LVIII (LXII), f. 3, 2012 49 Fisher D. M., Borschel G. H., Curtis C. G., Clarke H.M., Evaluation of Elbow Flexion as a Predictor of Outcome in Obstetrical Brachial Plexus Palsy. Plast. Reconstr. Surg., 120, 1585-90 (2007). Marciuk G.I., Metode de analiză numerică, Ed. Academiei, Bucureşti, 1983. Tannous R.E., Bandak F. A., Toridis T. G., Eppinger R. H., A Three-Dimensional Finite Element Model of the Human Ankle: Development and Preliminary Application to Axial Impulsive Loading, SAE 962427, Proceedings of the 40th Stapp Car Crash Conference, 1996, pp. 219–238. Wagenaar R.C., von Emmerik R.E.A., Dynamics of Movement Disorders. Human Movement Science, 15, 161-175 (1996). METODE DE CORECŢIE ALE MODELULUI BIOMECANIC ÎN CAZUL MIŞCĂRII ANTEBRAŢULUI (Rezumat) Simplificările realizate în cazul modelelor biomecanice conduc la necesitatea corecţiei modelelor realizate prin compararea permanentă cu valorile înregistrate experimental. Sunt discutate trei metode de corecţie a modelului biomecanic cu scopul de a transforma modelul experimental într-unul teoretic. În situaţia în care este necesară comparaţia a două curbe, una experimentală obţinută prin valori relative şi una teoretică obţinută prin valori absolute, interpolarea poate permite aflarea unei funcţii de interpolare convenabile, aceiaşi pentru ambele curbe comparate, în care coeficienţii funcţiei matematice diferă de la o curbă la alta. Funcţia de interpolare trebuie să satisfacă condiţia de a reprezenta cea mai bună aproximare între oricare două curbe (experimentală şi teoretică).
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BULETINUL INSTITUTULUI POLITEHNIC D
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Page 8 and 9: PETRU CÂRLESCU, VASILE DOBRE, IOAN
Page 10 and 11: 2 Niooleta Negoescu continue telle
Page 12 and 13: 4 Niooleta Negoescu min{ μ( Tu , T
Page 14 and 15: 6 Niooleta Negoescu Observation 8.
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Page 18 and 19: 10 Ion Crăciun ∂ ∂ ∂ ∂ ∇
Page 20 and 21: 12 Ion Crăciun ⎧σ ji = ( μ+ α
Page 22 and 23: 14 Ion Crăciun If we introduce the
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Page 30 and 31: 22 Ion Crăciun Ignaczak, J., Tenso
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