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The risk assessment consisted of ve steps: 1. Identify the factors in uencing seismic performance of the storage tanks: frequency of earthquakes, variability of soil properties, behavior of the tanks during earthquakes, performance of the foundation soils during earthquakes, performance of the re walls and revetments during earthquakes, performance of other site facilities designed to prevent spills, and so forth. 2. Analyze earthquake source models to establish seismic hazard at the site and establish levels of ground shaking and their respective probabilities of being exceeded. 3. Develop an event tree for a typical patio; the event tree is used to illustrate how various facilities in a tank patio interact to lead to system failure. 4. Build a numerical (Monte Carlo) simulation of the entire site to analyze random behavior of each key component; run the model through many realizations to develop a statistical sample of the behavior of the site and facility. 5. Compare the predicted probability of an off-site spill and the corresponding consequences to other risk; use the comparison and the annual expected monetary loss to judge the acceptability of the risk faced at this site. The seismic hazard at the site was developed using methods similar to those based on Cornell s (1968) work. The outcome of this hazard assessment is shown in Figure 22.17 as a complementary cumulative distribution (CCDF) of peak surface ground acceleration. For the numerical computation, this curve was approximated by the four discrete levels shown in Table 22.2.

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Sparsity has recently received much attention for extending existing algorithms to induce sparse solutions [65 67]. L1 -norm penalty has been used in regression [68], known as LASSO, and SVM [69, 70] to achieve model sparsity. Sparsity often leads to easy interpretation and good generalization ability of the resulting model. Sparse Fisher LDA has been proposed in reference 35, for binary-class problems. Based on the equivalence relationship between LDA and MLR, a multiclass sparse LDA formulation was proposed in reference 71 and an entire solution path for LDA was also obtained through the LARS algorithm [72]. The discussions in this chapter focus on supervised approaches. In the unsupervised setting, LDA can be applied to nd the discriminant subspace for clustering, such as K-means clustering. In this case, an iterative algorithm can be derived alternating between clustering and discriminant subspace learning via LDA [73 75].

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The integer division operator (\) divides the first operand by the second and returns the result, discarding the remainder. The second operand cannot be zero. Integer division is as straightforward as when you used it in grade school:

10 \ 3 returns 3 (10/3 is 15 \ 8 returns 1 (15/8 is 15 \ 5 returns 3 (15/5 is 5 \ 7 returns 0 (5/7 is 0 3 with remainder 1) 1 with remainder 7) 3 with remainder 0) with remainder 5)

1

Exponent operator (^)

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Interestingly, it can be shown that this iterative procedure can be simpli ed and is essentially equivalent to kernel K-means with a speci c kernel Gram matrix [76]. When the data in question are given as high-order representations such as 2D and 3D images, it is natural to encode them using high-order tensors. Discriminant tensor factorization, which is a two-dimensional extension of LDA, for a collection of twodimensional images has been studied [77]. It was further extended to higher-order tensors in reference 78. However, the computational convergency of these iterative algorithms [77, 78] is not guaranteed. Recently, a novel discriminant tensor factorization procedure with the convergency property was proposed [79]. Other recent extensions on discriminant tensor factorization as well as their applications to image analysis can be found in reference 80.

The exponent operator (^) returns the first operand raised to the power of the second operand:

10 1

2 ^ 3 returns 8 (2 to the third power is 8) 5 ^ 5 returns 3125 (5 to the fifth power is 3125) 8 ^ 2 returns 64 (8 squared is 64)

Wpt 1 ( m)

Listing 11-15 uses this feature to return the newly inserted Identity column value from an Insert trigger.

CREATE TRIGGER tI_SalesOrder ON SalesOrder AFTER INSERT AS BEGIN SELECT SalesOrderID FROM Inserted END

+ ||m||2 . (7.14)

One very important thing about this trigger is that it is useful only if you insert a single sales order in your SQL statement. Remember that this is a statement-level trigger, so it fires only once for the entire SQL statement that triggers it. Most systems insert sales orders one at a time, and the only time that you typically need the value of an Identity column is if you insert a sales order and then want to add line items to that sales order and, therefore, must have the foreign key (the Identity value).

10 2

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