Sentence examples for product vector from inspiring English sources

In a previous paper by the authors, the inner product vector (IPV) was proposed as a damage detection algorithm which uses cross correlation functions between response measurements.

With g parameters, the identifiability analysis of the cross product vector based direction finding algorithms of the general SSEMVS array is carried out.

The other rules of vector manipulation are subtraction, multiplication by a scalar, scalar multiplication (also known as the dot product or inner product), vector multiplication (also known as the cross product), and differentiation.

Then a new concept of inner product vector (IPV) is introduced using the cross correlation function of the measured vibration responses of the structure, and the corresponding damage detection method is proposed based on this vector.

It is shown that a proper choice of an inner product vector space, following the methodology developed by Ramkrishna and Amundson [14, 15], leads readily to the desired solution and guarantees its completeness.

In previous paper by the authors, the inner product vector (IPV) was proposed as a damage detection algorithm which uses cross correlation functions between vibration responses under band pass white noise excitation.

And unlike the record industry, booksellers are itching to get their hands on these new product vectors.

By suitably locating a pivot location such that distribution of the utility-distance product vectors are uniformly positioned about a pivot, equilibrium can be achieved.

By the same concept, to maintain zero utility-distance product equilibrium, the sum of utility-distance product vectors by all the items is equally divided for the players (Figure 4).

Since the inner product between two tensor product vectors (each one encoding one domain peptide pair) can be rewritten as a product of two inner products (Jacob and Vert, 2008), we compute the inner product between vectors of any two PDZ domains or vectors of any two peptides.

Interestingly, a classical property of tensor products allows us to factorize the inner product between two tensor product vectors as follows: (2) This factorization dramatically reduces the burden of working with tensor products in large dimensions.