**2.3 OSBPL1B (ORP1L)**

**Structure, Tissue distribution and Intracellular localization:** ORP1L belongs to the subfamily II of OSBP/ORP homologues. ORP1L has an ORD, an FFAT motif, a PH domain and three ankyrin repeats.

While macrophages, brain, and lung are the areas where ORP1L is expressed most predominantly, it is also found in colon, kidney, and liver (Johansson, Bocher et al. 2003). ORP1L localizes to late endosomes.

**Molecular functions related to dyslipidemia:** Johansson et al. reported that ORP1L binds to Rab7, modifies its functional cycle, and can interfere with LE/lysosome organization and endocytic membrane trafficking (Johansson, Lehto et al. 2005).

They show that the GTPase Rab7, when bound to GTP, simultaneously binds to ORP1L and RILP to form a RILP-Rab7-ORP1L complex, which is required for the perinuclear localization of late endosomes/lysosomes (Johansson, Rocha et al. 2007). The later study of Rocha et al., went deeper in examining these processes more in detail. They found that the cholesterol levels in late endosomes are sensed by ORP1L and are lower in peripheral vesicles. Under low cholesterol conditions, ORP1L conformation induces the formation of endoplasmic reticulum (ER)- late endosome membrane contact sites. At these sites, the ER protein VAP (VAMP [vesicle-associated membrane protein]-associated ER protein) can interact in trans with the Rab7-RILP complex to remove p150 (Glued) and associated motors. late endosomes then move to the microtubule plus end. Under high cholesterol conditions, as in Niemann-Pick type C disease, this process is prevented, and late endosomes accumulate at the microtubule minus end as the result of dynein motor activity. These data explain how the ER and cholesterol control the association of late endosomes with motor proteins and their positioning in cells (Rocha, Kuijl et al. 2009).

Recently, Vihervaara et al. have shown that ORP1L silencing in macrophage foam cells inhibits the efflux of lipoprotein-derived endocytosed cholesterol to apolipoprotein A-I, providing evidence for the involvement of ORP1L in both protein and lipid transport functions of the late endocytic compartments (Vihervaara, Uronen et al. 2011).

The multivesicular body(MVB) sorting pathway is known to be involved in many processes, including growth factor receptor down-regulation, exosome secretion, antigen presentation, the budding of enveloped viruses, and cytokinesis. Recently, Kobuna et al. have shown that knockdown of ORP1L induces the formation of enlarged MVBs in HeLa cells. They suggest that the proper cholesterol level of late endosomes/lysosomes generated by ORPs is required for normal MVB formation and MVB–mediated membrane protein degradation (Kobuna, Inoue et al. 2010).

**Epidemiological study:** Epidemiological study of ORP1L is not reported yet.


**5.3 Genetic Algorithms**

250 Bio-Inspired Computational Algorithms and Their Applications

**Third**, the difference between the desired and the actual output is computed, producing the network error. This error term is then passed backwards through the network to adjust the

Each network input unit takes a single numeric value, *i x* , which is usually scaled or normalized to a value between 0.0 and 1.0. This value becomes the input unit activation. Next, we need to propagate the data forward, through the neural network. For each unit in the hidden layer, we compute the sum of the products of the input unit activations and the weights connecting those input layer units to the hidden layer. This sum is the inner product (also called the dot or scalar product) of the input vector and the weights in the hidden unit. Once this sum is computed, we add a threshold value and then pass this sum through a nonlinear activation function, *f* , producing the unit activation *i y* . The formula for

computing the activation of any unit in a hidden or output layer in the network is

*i* ( ) *j i ij i y f sum x w* ==+

where *i* ranges over all the units leading into the *j*-th unit, and the activation function is

( ) 1 1 *j j sum f sum*

As mentioned earlier, we use the *S*-shape sigmoid or logistic function for *f*. The formula for

*wij j i* Δ =ηδ

error signal for that unit, and *i y* is the output or activation value of unit i. For units in the output layer, the error signal is the difference between the target output *j t* and the actual

( ) ( )( ) ( ) ´ 1 *j j jj j j jj j*

For each unit in the hidden layer, the error signal is the derivative of the activation function multiplied by the sum of the products of the outgoing connection weights and their

> ( ) ´ *j j j k jk*

down on oscillation of the weight change becomes a combination of the current weight

weight change. This complicates the implementation because we now have to store the

 δ

α

output *i y* multiplied by the derivative of the logistic activation function.

δ

where k ranges over the indices of the units receiving *j*-th unit´s output signal.

A common modification of the weight update rule is the use of a momentum term

*e* − = +

η

=− =− − *t y f sum t yy y* (37)

θ

(34)

(35)

δis the

α, to cut

*y* (36)

is the learn rate parameter, *j*

= *f sum w* (38)

ranges from 0 to 1) of the previous

connection weights.

calculating the changes of the weights is

where *wij* is the weight connecting unit i to unit j,

δ

corresponding error signals. So for the hidden unit j.

change, computed as before, plus some fraction (

weight changes from the prior step.

In this section a brief description of a simple genetic algorithm is given. Genetic algorithms are based on concepts and methods observed in nature for the evolution of the species. Genetic algorithms were brought to the artificial intelligence arena by Goldberg [6], [24]. They apply certain operators to a population of solutions of the problem to be solved, in a such a way that the new population is improved compared to the previous one according to a certain criterion function *J* [5], [1], [6], [24]. Repetition of this procedure for a preselected number of iterations will produce a last generation whose best solution is the optimal solution to the problem.

The solutions of the problem to be solved are coded in the *chromosome* and the following operations are applied to the coded versions of the solutions, in this order:

*Reproduction.* Ensures that, in probability, the better a solution in the current population is, the more replicates it has in the next population,

*Crossover.* Selects pair of solutions randomly, splits them in a random position, and exchanges their second parts.

*Mutation.* Selects randomly an element of a solution and alters it with some probability. It helps to move away from local minima.

Besides the coding of the solutions, some parameters must be set up:

The performance of the GA depends greatly on these parameters, as well as on the coding of the solutions in the chromosome. The solutions can be coded in some of the following formats:

*Binary.* Bit strings represent the solution(s) of the problem. For instance, a chromosome could represent a series of integer indexes to address a database, or the value of a variable(s) that must be integer, or each bit could represent the state (present-absent) of a part of an architecture that is being optimized, and so on.

*Real valued.* The bit strings represent the value of a real valued variable, in fixed of floating point.

The aspect of one chromosome could be like this: C = {100101010101010101}; the interpretation will vary in accordance with the coding scheme selected to represent the knowledge domain of the problem. For instance, it might represent a set of six indices of three bits each one; or it could have a meaning with all the bits together, representing an 18 bit code.

Optimal Feature Generation with

recognition system.

**6.1 Methodology**

the disposal of the next stage.

algorithm to determinate discriminant frequency bands.

Genetic Algorithms and FLDR in a Restricted-Vocabulary Speech Recognition System 253

AR/ARMA coefficients, Fourier coefficients, Cepstral coefficients, Mel Spectral Coefficients, and others [31]. In many the cases, the coefficients are computed over a short-time window (typically 10 ms) and over the voiced segments of the speech signal. As well as for the feature generation, for the classifier several options are available: Hidden Markov Models HHM (perhaps the most popular), neural networks (backpropagation, self-organizing maps, radial basis, etc.), support vector and other kernel machines, Gaussian mixtures, Bayesian type classifiers, LVQ, and others. It should be noted that this list of choices of each element in the pattern recognition chain is by no means extensive. Please note that by simply considering all the combinations of variables, features and classifiers mentioned here, it is easily seen that there exist too many ways to implement a restricted-vocabulary speech

Fig. 8. Block diagram showing the optimal feature generation with the GA and FLDR.

Figure 8 shows a block diagram of the whole speech recognition system. The system works in two phases: training and operation. The blocks of the training-phase are described below. Lexicon Acquisition and Normalization. In this stage, the set **L** = {**w0**,**w1**,**w2**,…, **wM-1**} of **M** words that will comprise the lexicon to be recognized is acquired from the speaker(s) that will use the system; vectors containing the digitized versions of the voice signals will be at

Power Spectrum. The power spectrum has been traditionally the source of features for speech recognition; here, the power spectrum of the voice signals will be used by the genetic

Optimal Feature Generation. The features selected here are a) the energy **E** of eight to twelve frequency regions (sub-bands) of the spectrum, b) the bandwidth (BW) of each sub-band, and c) the central frequency (FC) of the sub-bands. See Figure 9. Sub-band processing in speech have been previously used, but in different manners [32], [33], [34]. A genetic algorithm with elitism is used here to select each bandwidth (*BW*) its central frequency (*Fc*) and a number of sub-bands. The main parameters of the genetic algorithm are listed in table 1. The cost function of the GA is an expression aimed at maximizing the Fisher's

linear discriminant ratio (FLDR). The use of the FLDR in the cost function ensures

The primary reason of the success of genetic algorithms is its wide applicability, easy use and global perspective [6], [24], [25]. The next is the listing of a simple genetic algorithm.

The genetic algorithms find application in the field of speech processing via the solution to the problem of variable and feature selection [11], [14], [26], [27], [28], [29], [30].

## **6. Restricted-vocabulary speech recognition system**

The expression restricted-vocabulary speech recognition refers to the recognition of repetitions of spoken words that belongs to a limited set of words within a semantic field. This means that the words have connected meanings, for instance, the digits = {0,1,2,…, 9} or the days of the week={Saturday, Monday,…, Friday}. The applications of the recognition of limited size word-sets include voice-commanded systems, spoken entry and search for computer databases in warehouse systems, voice-assisted telephone dialing, man-machine interfaces, and others. The advantage of a system developed for a specific semantic field is that it can be built to be much more accurate than those constructed for the general speech recognition, also requiring less extensive training sets.

Restricted-vocabulary speech recognition is also an important research topic because of the intricacies involved in the underlying pattern recognition problem: variable selection, feature generation/selection and classifier selection. Variable selection is mostly restricted to select the raw digitized voice signal as the variable; alternatively, the surrounding environmental noise could be used as another variable for noise cancelation purposes. Feature generation has been carried out by obtaining linear predictive coefficients (LPC), AR/ARMA coefficients, Fourier coefficients, Cepstral coefficients, Mel Spectral Coefficients, and others [31]. In many the cases, the coefficients are computed over a short-time window (typically 10 ms) and over the voiced segments of the speech signal. As well as for the feature generation, for the classifier several options are available: Hidden Markov Models HHM (perhaps the most popular), neural networks (backpropagation, self-organizing maps, radial basis, etc.), support vector and other kernel machines, Gaussian mixtures, Bayesian type classifiers, LVQ, and others. It should be noted that this list of choices of each element in the pattern recognition chain is by no means extensive. Please note that by simply considering all the combinations of variables, features and classifiers mentioned here, it is easily seen that there exist too many ways to implement a restricted-vocabulary speech recognition system.

Fig. 8. Block diagram showing the optimal feature generation with the GA and FLDR.

### **6.1 Methodology**

252 Bio-Inspired Computational Algorithms and Their Applications

The primary reason of the success of genetic algorithms is its wide applicability, easy use and global perspective [6], [24], [25]. The next is the listing of a simple genetic algorithm.

The genetic algorithms find application in the field of speech processing via the solution to

The expression restricted-vocabulary speech recognition refers to the recognition of repetitions of spoken words that belongs to a limited set of words within a semantic field. This means that the words have connected meanings, for instance, the digits = {0,1,2,…, 9} or the days of the week={Saturday, Monday,…, Friday}. The applications of the recognition of limited size word-sets include voice-commanded systems, spoken entry and search for computer databases in warehouse systems, voice-assisted telephone dialing, man-machine interfaces, and others. The advantage of a system developed for a specific semantic field is that it can be built to be much more accurate than those constructed for the general speech

Restricted-vocabulary speech recognition is also an important research topic because of the intricacies involved in the underlying pattern recognition problem: variable selection, feature generation/selection and classifier selection. Variable selection is mostly restricted to select the raw digitized voice signal as the variable; alternatively, the surrounding environmental noise could be used as another variable for noise cancelation purposes. Feature generation has been carried out by obtaining linear predictive coefficients (LPC),

the problem of variable and feature selection [11], [14], [26], [27], [28], [29], [30].

**6. Restricted-vocabulary speech recognition system**

recognition, also requiring less extensive training sets.

Figure 8 shows a block diagram of the whole speech recognition system. The system works in two phases: training and operation. The blocks of the training-phase are described below.

Lexicon Acquisition and Normalization. In this stage, the set **L** = {**w0**,**w1**,**w2**,…, **wM-1**} of **M** words that will comprise the lexicon to be recognized is acquired from the speaker(s) that will use the system; vectors containing the digitized versions of the voice signals will be at the disposal of the next stage.

Power Spectrum. The power spectrum has been traditionally the source of features for speech recognition; here, the power spectrum of the voice signals will be used by the genetic algorithm to determinate discriminant frequency bands.

Optimal Feature Generation. The features selected here are a) the energy **E** of eight to twelve frequency regions (sub-bands) of the spectrum, b) the bandwidth (BW) of each sub-band, and c) the central frequency (FC) of the sub-bands. See Figure 9. Sub-band processing in speech have been previously used, but in different manners [32], [33], [34]. A genetic algorithm with elitism is used here to select each bandwidth (*BW*) its central frequency (*Fc*) and a number of sub-bands. The main parameters of the genetic algorithm are listed in table 1. The cost function of the GA is an expression aimed at maximizing the Fisher's linear discriminant ratio (FLDR). The use of the FLDR in the cost function ensures

Optimal Feature Generation with

word classification.

**6.2 System design**

energy can be calculated as:

voice in real-time a word can be immediately recognized.

In this section the details of the system design will be presented.

Consider the Fourier Transform of a signal:

filter bank that will operate in the time domain is:

by *K* matrices of *N* rows and 2000 columns.

**6.2.1 Characterization of the frequency spectrum using sub-bands**

Genetic Algorithms and FLDR in a Restricted-Vocabulary Speech Recognition System 255

where *x[i]* is the time domain signal with and *X[k]* is its modified frequency espectrum, which is found by taking the DFT of the signal and dividing the first and last frequencies by the square rooth of two. Therefore, all that have to be done is to filter the sub-bands out of the time signals and then to calculate the energy in each sub-band. This can be perfectly accomplished by a bank of digital band-pass filters whose parameters match the parameters found by the genetic algorithm, and the advantage is that at the end of the last sample of

In the Operation Phase, the three first stages operate simultaneously at each time a sample of the voice is acquired, and occur between two successive sampling intervals; in the first stage the voice signal is acquired and a normalization coefficient *Nc* is updated with the maximum value of the signal; the block labeled filtering represent the action of the filter bank and the outgoing signal is squared and added sample by sample. At the end of a voiced segment of sound, the third block provides to a classifier with the set of features for

> *S Fst* ( ) () ω

Now consider the frequency spectrum split in sub-bands, as shown in Figure 9. Please notice in Figure 9 that *S(*w*)* has been normalized to unitary amplitude. For each sub-band, the

> = 0 *ub*

> > *lb*

where *Ei*, is the energy of the *i*-th sub-band, *k0* is a constant proportional to the bandwidth (*BWi*) of the *i*-th sub-band, *lb* and *ub* are the lower and upper bounds of the *i-*th sub-band.

In which *M* is the number of sub-bands, and *Srn* is the reduced version of the *n*-th *S(*ω*)*. In order to characterize a word in the vocabulary, *N* samples of *s(t)* must be entered. From the example in Figure 9 and without loss of generality, the set of parameters of the respective

The number of filters to be applied in the time-domain signal in this case is L. For a vocabulary of *K* words (*K* small), the spectra in which the solution must be searched is given

*i*

The feature vector of the *n-th* utterance of *s(t)* in the frequency domain becomes:

( ) ( )2

= ( ) (40)

*Ek S ω*  (41)

*Srn M* ( ) *ω* = [*E*1234 *E E E E* ] (42)

1 12 23 3 = [ ] *BF C BW C BW C BW C BW L L* (43)

Table 1. Parameters of the Genetic Algorithm.

increasing both, class separability and cluster compactness between classes ω0 and ω1, being ω0 the class of the word to be recognized and ω1 the class of the rest of the *M*-1 words. The FLDR was described here in section 4.1, and the expression adopted here is the equation (31):

$$J\_2 = \frac{\left| \mathbf{S}\_b \right|}{\left| \mathbf{S}\_w \right|} = \left| \mathbf{S}\_w^{-1} \mathbf{S}\_b \right| $$

where |*Sb*| is the determinant of the inter-class covariance matrix and |*Sb*|is the determinant of the intra-class covariance class. At the end of the evolutionary process the genetic algorithm produced a set of vectors with the parameters [*E, BW, Fc*] and the number of sub-bands, between 8 and 12.

Fig. 9. Sub-band spectrum division to separate the power spectra in discriminant regions.

*Bank-filter Design.* During the operation phase, the calculation of the power espectrum of the incoming voice signals is not practical for real-time response because the discrete Fourier transform (DFT [2], [3]) requires too much time to be calculated, and the fast Fourier Transform (FFT [2], [3]) requires that the number of samples to be a power of 2, requiring zero padding most of the times. Instead of using the power spectrum, in this application it is used the Parseval's theorem, described in section 4.2, which states that the energy in time and frequency is equal. In discrete form, we recall equation (32):

$$\sum\_{i=0}^{N-1} \mathbf{x}[i]^2 = \frac{2}{N} \sum\_{k=0}^{N/2} \left\| \mathbf{X}[k] \right\|^2$$

where *x[i]* is the time domain signal with and *X[k]* is its modified frequency espectrum, which is found by taking the DFT of the signal and dividing the first and last frequencies by the square rooth of two. Therefore, all that have to be done is to filter the sub-bands out of the time signals and then to calculate the energy in each sub-band. This can be perfectly accomplished by a bank of digital band-pass filters whose parameters match the parameters found by the genetic algorithm, and the advantage is that at the end of the last sample of voice in real-time a word can be immediately recognized.

In the Operation Phase, the three first stages operate simultaneously at each time a sample of the voice is acquired, and occur between two successive sampling intervals; in the first stage the voice signal is acquired and a normalization coefficient *Nc* is updated with the maximum value of the signal; the block labeled filtering represent the action of the filter bank and the outgoing signal is squared and added sample by sample. At the end of a voiced segment of sound, the third block provides to a classifier with the set of features for word classification.

#### **6.2 System design**

254 Bio-Inspired Computational Algorithms and Their Applications

Mutation Gaussian ( 1-2 , 1-2) (scale,shrink)

Crossover Scattered, one-point and two-points

2

increasing both, class separability and cluster compactness between classes ω0 and ω1, being ω0 the class of the word to be recognized and ω1 the class of the rest of the *M*-1 words. The FLDR was described here in section 4.1, and the expression adopted here is the equation

*b*

*w S J SS S* − = =

where |*Sb*| is the determinant of the inter-class covariance matrix and |*Sb*|is the determinant of the intra-class covariance class. At the end of the evolutionary process the genetic algorithm produced a set of vectors with the parameters [*E, BW, Fc*] and the number

Fig. 9. Sub-band spectrum division to separate the power spectra in discriminant regions.

and frequency is equal. In discrete form, we recall equation (32):

*Bank-filter Design.* During the operation phase, the calculation of the power espectrum of the incoming voice signals is not practical for real-time response because the discrete Fourier transform (DFT [2], [3]) requires too much time to be calculated, and the fast Fourier Transform (FFT [2], [3]) requires that the number of samples to be a power of 2, requiring zero padding most of the times. Instead of using the power spectrum, in this application it is used the Parseval's theorem, described in section 4.2, which states that the energy in time

1 /2 2 2

*xi Xk N*

2 [] [ ]

0 0

= = =

*N N i k*

−

1

*w b*

*Parameter Value*

Population 20-120

Selection Elitism

Table 1. Parameters of the Genetic Algorithm.

of sub-bands, between 8 and 12.

(31):

In this section the details of the system design will be presented.

#### **6.2.1 Characterization of the frequency spectrum using sub-bands**

Consider the Fourier Transform of a signal:

$$S(\mathfrak{so}) = F\left(s\left(t\right)\right) \tag{40}$$

Now consider the frequency spectrum split in sub-bands, as shown in Figure 9. Please notice in Figure 9 that *S(*w*)* has been normalized to unitary amplitude. For each sub-band, the energy can be calculated as:

$$E\_l = k\_0 \sum\_{\rm lb}^{\rm ub} \left( \mathbf{S} \left( \boldsymbol{\omega} \right)^2 \right) \tag{41}$$

where *Ei*, is the energy of the *i*-th sub-band, *k0* is a constant proportional to the bandwidth (*BWi*) of the *i*-th sub-band, *lb* and *ub* are the lower and upper bounds of the *i-*th sub-band. The feature vector of the *n-th* utterance of *s(t)* in the frequency domain becomes:

$$\mathbf{Sr}\_n(\omega) = \begin{bmatrix} \mathbf{E}\_1 \ \mathbf{E}\_2 \ \mathbf{E}\_3 \ \mathbf{E}\_4 \cdots \mathbf{E}\_M \end{bmatrix} \tag{42}$$

In which *M* is the number of sub-bands, and *Srn* is the reduced version of the *n*-th *S(*ω*)*. In order to characterize a word in the vocabulary, *N* samples of *s(t)* must be entered. From the example in Figure 9 and without loss of generality, the set of parameters of the respective filter bank that will operate in the time domain is:

$$BF = \begin{bmatrix} \mathbf{C}\_1 \ \mathbf{B} \mathbf{W}\_1 \ \mathbf{C}\_2 \ \mathbf{B} \mathbf{W}\_2 \ \mathbf{C}\_3 \ \mathbf{B} \mathbf{W}\_3 \cdots \mathbf{C}\_L \ \mathbf{B} \mathbf{W}\_L \end{bmatrix} \tag{43}$$

The number of filters to be applied in the time-domain signal in this case is L. For a vocabulary of *K* words (*K* small), the spectra in which the solution must be searched is given by *K* matrices of *N* rows and 2000 columns.

Optimal Feature Generation with

[5].

**Female**

**Male**

**7. Results**

c. Compute the covariance matrix, Si = cov(*C*i).

dM(x,μi) = (x-μi)\*Σi

Processor mounted on an experimentation card.

**Set**

**G1 L2 Training**

the feature vector *x* of an incoming command is:

Genetic Algorithms and FLDR in a Restricted-Vocabulary Speech Recognition System 257

To make the real-time implementation, a digital system must sample the input microphone continuously. Each sample of a command must be filtered by each filter (sub-band extraction). The integral of the energy of the signal leaving each filter over the period of the command is used to create the feature vector of that command, that is, *x* in equation 44. The feature vector is compared to each model in the vocabulary, and the command is recognized as the one with the minimum *dM* score. This is the so-called "minimum distance classifier"

To test the system, the following lexicons were used: *L0*={faster, slower, left, right, stop, forward, reverse, brake}, *L1*={zero, one, two, three, four, five, six, seven, eigth, nine}, L2={rápido, lento, izquierda, derecha, alto, adelante, reversa, freno}, L3 = {uno, dos, tres, cuatro, cinco}. In all the lexicons, 3 male and 3 female volunteers were enrolled. They donated 116 samples of each word, 16 for training and 100 for testing. To demostrate the power of our approach we used the minimum distance classifier with the Mahalanobis distance. In all the cases, the genetic algorithm was ran 30 times to find the best response in the training set. During the training phase, the leave-one-out method was used to exploit the limited size of the training set [1]. Table 2 summarizes the results. In columns 5 and 7 are shown the comparison against a backpropagation neural network using as features Cepstral coefficients. The experiments were done using Matlab(R) and its associated toolboxes of genetic algorithms, neural networks and digital signal processing. The real-time implementation was done with a TMS320LF2407 Texas Instruments(R) Digital Signal

> **Testing Set**

1Gender, 2 Lexicon, 3Minimum distance classifier, 4Backpropagation neural network [1] 8-32-K

Table 2. Percentage (%) of correct classification with 4 lexicons, 2 languages, 6 persons, male

neurons per layer, K according to the experiment, one neuron for each word in L.

and female voices, 2 classifiers. Simulations and real time implementation.

0 100 97 92 94 90 1 100 98 90 95 90 2 100 100 97 94 89 3 100 100 91 95 88

0 100 100 94 96 89 1 100 100 90 95 90 2 100 99 92 94 90 3 100 98 92 93 88

d. At any time, the Mahalanobis distance between the model of the *i*-th command and

Simulations Real-time on DSP MDC3 BPNN4 MDC3 BPNN4

> **Real scenario**

**Real scenario**

**Testing Set**

#### **6.2.2 Genetic algorithm set up**

#### **<IP_ADDRESS> Coding the chromosome**

The chromosome is comprised of the parameters of the filter bank described in equation 43. The number of sub-bands is fixed, and each one of the centers and bandwidths are subject to the genetic algorithm. Real numbers are used.

#### **<IP_ADDRESS> The cost function**

The cost function is given by equation (32), criterion *J2*. The goal is to maximize J2 as a function of the centers and bandwidths.

#### **<IP_ADDRESS> Restrictions**

The following restrictions apply:

*R1*. Sub-bands overlapping < = 50Hz,

*R2*. Bandwidth is limited to range from 40 to 400 Hz, varying according to the performance of the genetic algorithm.

#### **<IP_ADDRESS> Operating parameters of the genetic algorithm**

The main parameters of the genetic algorithm are summarized in the Table 1. The values of the parameters are given according to the best results obtained by experimentation. The genetic algorithm was ran in Matlab®, using the genetic algorithms toolbox and the *gatool*  guide. The nomenclature of the parameters in Table 1 is the one used by Matlab®.

#### **<IP_ADDRESS> Application's algorithm**

To make operational de methodology described so far, the following steps apply:

	- a. Construct a matrix *C* of 15-20 rows of *Srn(*w*)* and *M* columns (one row per sample of the command, one column per sub-band selected by the AG),
	- b. Compute the mean value overall the samples, to find the average energy per subband, this is the feature vector of the command (μi)

$$\text{d}M(\mathbf{x},\mu\_i) = (\mathbf{x}\cdot\mu\_i)^\* \Sigma\_i^{-1\*} (\mathbf{x}\cdot\mu\_i)^{-1} \tag{44}$$

To make the real-time implementation, a digital system must sample the input microphone continuously. Each sample of a command must be filtered by each filter (sub-band extraction). The integral of the energy of the signal leaving each filter over the period of the command is used to create the feature vector of that command, that is, *x* in equation 44. The feature vector is compared to each model in the vocabulary, and the command is recognized as the one with the minimum *dM* score. This is the so-called "minimum distance classifier" [5].


**2.5. Estimation of the confidence interval and risk analysis for wind speed and solar irradiation scenarios**

From the power system planning viewpoint, the important aim is to reduce as far as possi‐ ble the uncertainty and risk associated with generation and power supply. The risk is more crucial when the ex-ante planned generation is less than the ex-post actual generation. The error in the forecast data can be estimated with a level of confidence (LC), in order to deter‐ mine a reliable level of generation to be considered in the planning stage. Here, the confi‐ dence interval method [26], known in risk assessment problems is proposed as a constraint to specify a lower and upper band for the wind speed and solar irradiation scenarios. For example, *LC=90%* means that the probability of forecast error (*Powerrisk*) being less than a definite value obtained from the distribution of forecast error

**Figure 7.** Hourly wind speed data in farm 1

(a)

(b)

**Kendall's tau correlation Freedom degree (υ) Linear correlation (ρ) Hour**

**Table 1.** The parameters of the two-variable t-Student copula distribution for wind speed of two farms at different

As mentioned earlier, besides the spatial correlation among different farms, the wind speed and solar irradiation random variables assigned to the scheduling time steps exhibit tempo‐ ral correlation, i.e., they are dependent on the condition of random variables at previous

**Figure 6.** Correlated samples for solar irradiation of two farms for 7 and 11 AM in fall season

**2.4. Time-series prediction of wind speed and solar irradiation**

hours of the day in fall season

110 New Developments in Renewable Energy

(*Powerrisk,*max) is more than 90%. The value of *Powerrisk,*max is referred to as the "Value at Risk"

Improved Stochastic Modeling: An Essential Tool for Power System Scheduling in the Presence of Uncertain

In order to calculate the *VAR* of forecast based on the defined *LC*, the PDF of forecast error should be analyzed. In fact, the maximum value of *Powerrisk,*max that satisfies equation (11) is chosen as the *VAR*. Given the forecast error PDF to be a normal distribution with mean *μe*

and standard deviation *σe*, the *VAR* is calculated by (Figure 11):

a

[ ] 100 *e e Pe z*a

 ³+ < m s

~ *risk*, *e e Power e z*

a

a = = + m s

is the maximum forecast error at which the condition is satisfied. The error is ex‐

pressed in terms of μe and *σe* where *zα* is the coefficient of σe. Then, this value is subtracted from and added to the forecasted data to give the lower band and upper band of the confi‐ dence interval, respectively. This band limits the scenarios generated for wind speed and so‐ lar irradiation and provides a reliable range for generation of scenarios that will be considered in the stochastic optimization process. Figure 12 and Figure 13 present an exam‐ ple of the distribution of forecast error of wind speed and solar irradiation at 11 AM, respec‐ tively. A representation of the confidence interval method applied to the wind speed data is

The final step of data processing before performing the stochastic optimization procedure is the scenario generation and reduction. This makes the main distinction between the deter‐ ministic programming and the stochastic programming approach. The deterministic pro‐ gramming deals with determined inputs and pre-defined parameters of the system model, whereas the stochastic programming combines the process of assignment of optimum val‐ ues to the control variables with the stochastic models of the existing random variables. The variables with stochastic behavior are indeed represented by a bunch of scenarios reflecting the most probable situations that is likely to occur for them. Here, in the scenario generation step, using the multivariate distribution function obtained, a large set of random vector sam‐ ples will be generated using the Monte Carlo simulation. Then, since all of the generated scenarios may not be useful and some of them may exhibit similarities and correlation with

*risk risk max* , *P Power Power LC* é ù £ > ë û (11)

= - 100 *LC* (12)

(13)

Renewables

113

http://dx.doi.org/10.5772/45849

*max* (14)

(*VAR*).

where *e* ~

depicted in Figure 14.

**2.6. Scenario generation and reduction**

**Figure 8.** Hourly solar irradiation data in farm 1

**Figure 9.** forecast results of wind power along with the actual data for one week

**Figure 10.** forecast results of wind power along with the actual data for one week

(*Powerrisk,*max) is more than 90%. The value of *Powerrisk,*max is referred to as the "Value at Risk" (*VAR*).

$$P\left[Power\_{risk} \le Power\_{risk, max}\right] > LC\tag{11}$$

In order to calculate the *VAR* of forecast based on the defined *LC*, the PDF of forecast error should be analyzed. In fact, the maximum value of *Powerrisk,*max that satisfies equation (11) is chosen as the *VAR*. Given the forecast error PDF to be a normal distribution with mean *μe* and standard deviation *σe*, the *VAR* is calculated by (Figure 11):

$$
\alpha = 100 - L\mathcal{C} \tag{12}
$$

$$P[\varepsilon \ge \mu\_{\varepsilon} + z\_{\alpha}\sigma\_{\varepsilon}] < \frac{a}{100} \tag{13}$$

$$\text{Power}\_{r\&\text{\textquotedblleft},\text{max}} = \stackrel{\text{\textquotedblleft}}{e} = \mu\_e + \text{z}\_a \sigma\_e \tag{14}$$

where *e* ~ is the maximum forecast error at which the condition is satisfied. The error is ex‐ pressed in terms of μe and *σe* where *zα* is the coefficient of σe. Then, this value is subtracted from and added to the forecasted data to give the lower band and upper band of the confi‐ dence interval, respectively. This band limits the scenarios generated for wind speed and so‐ lar irradiation and provides a reliable range for generation of scenarios that will be considered in the stochastic optimization process. Figure 12 and Figure 13 present an exam‐ ple of the distribution of forecast error of wind speed and solar irradiation at 11 AM, respec‐ tively. A representation of the confidence interval method applied to the wind speed data is depicted in Figure 14.

#### **2.6. Scenario generation and reduction**

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0

Time (hr)

Actual data Estimated data

Actual data Estimated data

0 20 40 60 80 100 120 140 160 0

0 20 40 60 80 100 120 140 160 0

Hour

Hour

> 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

> 0.2 0.4 0.6 0.8 1 1.2 1.4

**Figure 10.** forecast results of wind power along with the actual data for one week

Solar power (p.u.)

**Figure 9.** forecast results of wind power along with the actual data for one week

Wind power (p.u.)

Solar irradiation (W/m

**Figure 8.** Hourly solar irradiation data in farm 1

112 New Developments in Renewable Energy

2

)

The final step of data processing before performing the stochastic optimization procedure is the scenario generation and reduction. This makes the main distinction between the deter‐ ministic programming and the stochastic programming approach. The deterministic pro‐ gramming deals with determined inputs and pre-defined parameters of the system model, whereas the stochastic programming combines the process of assignment of optimum val‐ ues to the control variables with the stochastic models of the existing random variables. The variables with stochastic behavior are indeed represented by a bunch of scenarios reflecting the most probable situations that is likely to occur for them. Here, in the scenario generation step, using the multivariate distribution function obtained, a large set of random vector sam‐ ples will be generated using the Monte Carlo simulation. Then, since all of the generated scenarios may not be useful and some of them may exhibit similarities and correlation with other scenarios, the scenario reduction techniques are applied to eliminate low-probability scenarios and merge the similar ones to extract a limited number of scenarios keeping the whole probable region of the variables covered. Furthermore, the scenario reduction techni‐ que increases the computational efficiency of the optimization process. The most wellknown methods for scenario reduction are fast backward, fast forward/backward and the fast forward method [27-28]. The first step in scenario reduction is clustering. By clustering, the scenarios which are close to each other are put in one cluster. In the following, we present a description on the fast backward method.

The backward method is initialized by selection of scenarios as the candidates to be elimi‐ nated. The selection criterion is based on the minimum value for the product of each sam‐ ple's probability by the probabilistic distance of that sample to others. The probabilistic distance of each sample to others is considered as the minimum distance of that sample to each of the other samples in the same set. In the next step, the same analysis is performed on the remaining scenarios, however, in this step the product of the eliminated scenarios proba‐ bilities in the previous step by the distance of the current sample to other samples are also included. This process continues until the probabilistic distance obtained in each step (itera‐ tion) would be less than a predefined value as the convergence criterion. Mathematically speaking, the algorithm can be summarized as follows:

$$\begin{aligned} &j^{[0]}:=0\\ &j^{[0]}:=0\\ &Z^i = p\_i \min\_{i \neq j} \left\| \boldsymbol{\xi}^i - \boldsymbol{\xi}^j \right\| \; ; \; i = 1, \ldots, N\\ &j^{[1]} = \text{Min } Z^i \quad ; \; i = 1, \ldots, N\\ &\mathbf{STEP} \quad \textbf{[1]}:\\ &j\_i \in \arg\min\_{\boldsymbol{\ell}^{[i-1]} \atop j^{[i-1]} \neq \boldsymbol{1}} \sum\_{j^{[i-1]} \cup \boldsymbol{1}} p\_i \underset{j^{[i-1]} \in \mathbb{J}}{\text{Min}} \left\| \boldsymbol{\xi}^k - \boldsymbol{\xi}^j \right\| \end{aligned} \tag{15}$$

$$\begin{aligned} &j \underset{j^{[i-1]} \in \mathbb{J}}{\text{Min}} \sum\_{j^{[i-1]} \in \mathbb{J}} p\_i \underset{j^{[i-1]} \in \mathbb{J}}{\text{Min}} \left\| \boldsymbol{\xi}^k - \boldsymbol{\xi}^j \right\| \leq \boldsymbol{\varepsilon} \\ &j^{[i-1]} = j^{[i-1]} \cup j\_i \\ &\mathbf{E} \boldsymbol{\ell} \boldsymbol{\varepsilon} = \boldsymbol{I}^{[i-1]} \\ &\mathbf{END} \quad \textbf{END} \end{aligned} \tag{16}$$

overall objective function value to be optimized. As a comparison, the mean square error (MSE) of the calculated scenarios with respect to the real data is calculated in two cases. In the first case, the scenarios are obtained from the Copula distribution and within the calcu‐ lated confidence interval, based on the proposed framework. In the second case, the scenar‐ ios are generated from the single-variable distributions without modeling the underlying temporal and spatial correlations. Table 2 exhibits that the error in the first case is less than

Improved Stochastic Modeling: An Essential Tool for Power System Scheduling in the Presence of Uncertain

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115

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Wind power forecast error

that in the second case in three days under study.

**Figure 11.** Value at risk calculation based on the distribution of forecast error

20

**Figure 12.** Distribution of error for wind speed forecast at 11 AM

40

60

Number of samples

80

100

120

where *N* is the total number of scenarios, *n* is the number of remaining scenarios, and |J| = N – n. *ξ i* is scenario *i* with probability *pi* . Here, 1000 initial scenarios are generated based on the obtained Copula multi-variable distribution function within the calculated confidence interval. Then, the scenarios are reduced to 20 uncorrelated scenarios using SCENRED func‐ tion in GAMS software. Figure 15 and Figure 16 demonstrate the final set of scenarios for a day of scheduling.

The power generation scheduling problem including units with uncertain and volatile char‐ acteristic is commonly treated as a stochastic optimization problem. In this approach, the ob‐ jective function calculation is repeated per all final scenarios in each iteration of the optimization process, where the summation of these objective values commonly defines the overall objective function value to be optimized. As a comparison, the mean square error (MSE) of the calculated scenarios with respect to the real data is calculated in two cases. In the first case, the scenarios are obtained from the Copula distribution and within the calcu‐ lated confidence interval, based on the proposed framework. In the second case, the scenar‐ ios are generated from the single-variable distributions without modeling the underlying temporal and spatial correlations. Table 2 exhibits that the error in the first case is less than that in the second case in three days under study.

**Figure 11.** Value at risk calculation based on the distribution of forecast error

other scenarios, the scenario reduction techniques are applied to eliminate low-probability scenarios and merge the similar ones to extract a limited number of scenarios keeping the whole probable region of the variables covered. Furthermore, the scenario reduction techni‐ que increases the computational efficiency of the optimization process. The most wellknown methods for scenario reduction are fast backward, fast forward/backward and the fast forward method [27-28]. The first step in scenario reduction is clustering. By clustering, the scenarios which are close to each other are put in one cluster. In the following, we

The backward method is initialized by selection of scenarios as the candidates to be elimi‐ nated. The selection criterion is based on the minimum value for the product of each sam‐ ple's probability by the probabilistic distance of that sample to others. The probabilistic distance of each sample to others is considered as the minimum distance of that sample to each of the other samples in the same set. In the next step, the same analysis is performed on the remaining scenarios, however, in this step the product of the eliminated scenarios proba‐ bilities in the previous step by the distance of the current sample to other samples are also included. This process continues until the probabilistic distance obtained in each step (itera‐ tion) would be less than a predefined value as the convergence criterion. Mathematically

[ 1] [ 1] [ 1]

U

Î -

å

*i i*

arg *i i i*

*j Min p Min*

*J Min Z i N*

x x

= -= = =

*i k I Ii J i*

*r i i j i i j i*

¹

\

where *N* is the total number of scenarios, *n* is the number of remaining scenarios, and |J| =

the obtained Copula multi-variable distribution function within the calculated confidence interval. Then, the scenarios are reduced to 20 uncorrelated scenarios using SCENRED func‐ tion in GAMS software. Figure 15 and Figure 16 demonstrate the final set of scenarios for a

The power generation scheduling problem including units with uncertain and volatile char‐ acteristic is commonly treated as a stochastic optimization problem. In this approach, the ob‐ jective function calculation is repeated per all final scenarios in each iteration of the optimization process, where the summation of these objective values commonly defines the

min ; 1,..., ; 1,...,

*Z p i N*

\

xx

*r k l*

x x

*r k l*

(15)

 e

. Here, 1000 initial scenarios are generated based on

[ 1] [ 1] [ 1]

U U

*k I Ii J i i i*

*i i i*

*if Min p Min*

å

[ 1]

[ ] [ 1]

= =

**END**

N – n. *ξ i* is scenario *i* with probability *pi*

day of scheduling.

*JJ j Else J J*

present a description on the fast backward method.

114 New Developments in Renewable Energy

speaking, the algorithm can be summarized as follows:

[0]

**STEP [0] :**

*J*

: 0

=

[1]

**STEP [i] :**

**Figure 12.** Distribution of error for wind speed forecast at 11 AM

0 5 10 15 20 25 -2

Improved Stochastic Modeling: An Essential Tool for Power System Scheduling in the Presence of Uncertain

Renewables

117

http://dx.doi.org/10.5772/45849

Time (hr)

0 5 10 15 20 25 0

Time (hr)

Natural characteristics of wind and solar energy impose uncertainty in their design and op‐ eration. Hence, considering various possible scenarios in the model of these resources can lead to more realistic decisions. The uncertain parameters are expressed by probability dis‐ tributions, showing the range of values that a random variable could take, and also account‐ ing for the probability of the occurrence of each value in the considered range. Therefore, the way the random processes are modeled in terms of their PDF is a significant problem. The possible spatial correlations have been addressed and shown effective using the Copula

0

Solar irradiation W/m

**3. Conclusion**

2)

**Figure 15.** Final scenarios of wind speed within its confidence interval

**Figure 16.** Final scenarios of solar irradiation within its confidence interval

2

4

Wind speed (m/s)

6

8

10

**Figure 13.** Distribution of error for solar irradiation forecast at 11 AM

**Table 2.** MSE in Case 1 and 2 for the total power of two wind farms

**Figure 14.** Upper and lower band of confidence interval for wind speed data on one day

Improved Stochastic Modeling: An Essential Tool for Power System Scheduling in the Presence of Uncertain Renewables http://dx.doi.org/10.5772/45849 117

**Figure 15.** Final scenarios of wind speed within its confidence interval

**Figure 16.** Final scenarios of solar irradiation within its confidence interval

#### **3. Conclusion**

Solar power forecast error

0 5 10 15 20 25 -2

Time (hr)

**Figure 14.** Upper and lower band of confidence interval for wind speed data on one day

1.97e-3 1.4e-3 Day 1 5.99e-3 4.05e-3 Day 2 4.11e-3 3.31e-3 Day 3

**MSE in Case 2 MSE in Case 1**

100

**Figure 13.** Distribution of error for solar irradiation forecast at 11 AM

**Table 2.** MSE in Case 1 and 2 for the total power of two wind farms

Lower band Upper band Measured data Predicted data

0

2

4

Wind speed (m/s)

6

8

10

200

300

400

Number of samples

500

600

700

116 New Developments in Renewable Energy

Natural characteristics of wind and solar energy impose uncertainty in their design and op‐ eration. Hence, considering various possible scenarios in the model of these resources can lead to more realistic decisions. The uncertain parameters are expressed by probability dis‐ tributions, showing the range of values that a random variable could take, and also account‐ ing for the probability of the occurrence of each value in the considered range. Therefore, the way the random processes are modeled in terms of their PDF is a significant problem. The possible spatial correlations have been addressed and shown effective using the Copula method. Similarly, the possible temporal correlations have been taken into account using a time-series analysis. In summary, the overall framework can be listed as follows:

[5] S. Abedi*, et al.*, "Risk-Constrained Unit Commitment of Power System Incorporating

Improved Stochastic Modeling: An Essential Tool for Power System Scheduling in the Presence of Uncertain

Renewables

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[6] K. Methaprayoon*, et al.*, "An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty," *Industry Applications, IEEE‐*

[7] B. C. Ummels*, et al.*, "Impacts of wind power on thermal generation unit commitment and dispatch," *Energy Conversion, IEEE Transactions on,* vol. 22, pp. 44-51, 2007.

[8] C. L. Chen, "Optimal wind–thermalgenerating unit commitment," *Energy Conversion,*

[9] M. A. Ortega-Vazquez and D. S. Kirschen, "Estimating the spinning reserve require‐ ments in systems with significant wind power generation penetration," *Power Sys‐*

[10] E. M. Constantinescu*, et al.*, "A computational framework for uncertainty quantifica‐ tion and stochastic optimization in unit commitment with wind power generation,"

[11] [L. V. L. Abreu*, et al.*, "Risk-Constrained Coordination of Cascaded Hydro Units With Variable Wind Power Generation," *Sustainable Energy, IEEE Transactions on,* vol. 3,

[12] V. S. Pappala*, et al.*, "A stochastic model for the optimal operation of a wind-thermal power system," *Power Systems, IEEE Transactions on,* vol. 24, pp. 940-950, 2009. [13] H. Siahkali and M. Vakilian, "Stochastic unit commitment of wind farms integrated in power system," *Electric Power Systems Research,* vol. 80, pp. 1006-1017, 2010.

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**5. Multimodal tumor imaging and therapy**

There has been moderate impact of targeted therapies in glioma. The therapies that have demonstrated a significant survival benefit for gliomas in Phase III clinical trials, including radiation, chemotherapy (temozolomide and PCV [procarbazine, lomustine, vincristine]), and tumor-treating fields, are based on nonspecific targeting of proliferating cells. An emerging field in glioblastoma nuclear medicine is use of radionanoparticles. These radioactive nanocarriers can be used passively as a simple tumor brachytherapy or can be actively used with a specific targeting to vectorize a large amount of radioactivity. The targeting is usually directed against a glioblastoma-specific antigen or receptor. Antigen targets, like epidermal growth factor receptor (EGFR), tenascin, or DNA histone H1 complex. Radiolabeled antibodies and peptides hold promise for molecular radiotherapy but are often limited by a low payload resulting in inadequate delivery of radioactivity to tumor tissue and, therefore, inadequate therapeutic effect and adverse effects due irradiation of normal tissues [24]. Song et al. developed a synthetic method of radiolabeling indium-111 (111In) to epidermal growth factor (EGF)-gold nanoparticles ( 111In-EGF-Au NP) with a high payload [25]. By using radiolabeled nanoparticles, comparatively higher payloads are obtained due to large surface area to volume ratio. This results in multivalent effect of nanoparticles, thus accommodating a large number of targeting ligands, such as antibodies, peptides or aptamers on a single nanoparticle. This facilitates maximal binding to the molecular target in vivo, thus enhancing delivery of radioactivity to target tissue with improved imaging and therapeutic efficacy. PEGylation of nanoparticles and alteration of their surface properties improves their stability and mean residence time in vivo [26]. It also permits loading a combination of imaging, radiotherapeutic and/or chemotherapeutic moieties for multimodal tumor imaging and therapy [27]. Antibodies, radiolabeled antibodies, antibody fragments or peptides because of their small size easily penetrate surrounding normal tissues. Loading onto nanoparticles limits their penetration through normal vasculature and capillaries, thus minimizing their side-effects [28].

Different nanocarriers such as metallofullerenes, liposomes, or lipid nanocapsules have been used to deliver radionanoparticle passively. A typical metallofullerene (177Lu-DOTA-f-Gd3N@C80) radionanoparticles when administered by convection-enhanced delivery (CED) in brain tumor model showed an improved

survival time of more than 2.5 times that of the control group [29]. Similarly liposomes loaded with beta-negative emitters such rhenium-186 and demonstrated promising results when administered by CED in an orthotopic glioblastoma rat model [30]. Lipid nanocapsules loaded with rhenium-188 in a rat orthotopic model showed a significant survival benefit after intratumoral stereotactic injection at day 6 and CED injection at day 12 [31].

A recent approach using radionanoparticles consists of an active targeting approach where the nanoparticles are functionalized and directed against a tumor target. The aim of this active targeting is to optimize the spatial localization of the radioactivity close to the tumor cells. As an example, lipid nanocapsules can be loaded with rhenium-188 and coupled to a monoclonal antibody directed against the CXCR4 antigen. These CXCR4-recognizing immune-nanoparticles irradiate the tumor cells and have been shown to increase efficacy in an orthotopic mouse model. Recurrence for the passive protocol was observed at 65 versus 100 days for the active targeting approach, and this appears to be the most effective therapy with the longest measured time to progression [32].