determine each pixel belongs to background or foreground.Wis the weights between the pattern and summationneurons, which are used to point out with which a pattern belongs to the background or foreground. They areupdated when each new value of a pixel at a certain position received by implementing the following function:Wt+1ib=fc(1−βNpn)Wib+MAtβ!(37)Wt+1i f=(1−Wt+1ib)(38)whereWtibis the weight between theith pattern neuron and the background summation neuron at timet,βisthe learning rate,Npnis the number of the pattern neurons of BNN,fcis the following function:fc(x)1,x>1x,x≤1(39)MAtindicates the neuron with the maximum response (activation potential) at frame t, according to:MAt1,f or neuron with maximum response0,otherwise(40)Function …show more content…
The total frame is :T=Tno+To(41)The weights for the pattern neuron corresponding to the feature value are determined:WTib=(1−βNpn)TnoWoib+Toβ!(42)WTi f=1−WTib(43)whereWoibcorresponds to the initial weight set when the pattern is first observed. …show more content…
If it is not encountered forTnoframes, then the confidence that afeature value belongs to the background will decay from the maximum to(1−β/Npn)Tno.Activation and Replacement Subnets: The activation subnet contains two functions: first it can point outwhich net work has the maximun output and whether the maximum value exceeds the threshold. If the maximumvalue does not exceed the threshold then the background network will be inactivated and the weight of thepattern neuron is considered to be replaced. On the contrary, if exceeded, the feature is considered belonging to aforeground object.In the first layer, a single neron is used to indicate whether the network is activated or not. This layer contains, Vol. 1, No. 1, Article . Publication date: January 2018.
Such as, 2 2 2 , , r s s r r r s r r r L L R L R M L L M L PM L R Where rd s i u , , and r : are respectively, the stator voltage, stator current, rotor flux and rotor speed. The indices d, q indicates a direct and quadrate index according to the usual d-axis and q-axis components in the synchronous rotating frame. M L L R R r s r s , , , , and : are respectively, stator and rotor resistance, stator and rotor inductance, mutual inductance and total leakage factor. P, J, TL and f: are respectively, the number of pole pairs, the rotor inertia, the load torque and the friction coefficient.
V. EXPERIMENTAL SETUP & RESULTS The proposed dual T-NPC, dual PMSM topology and its modulation and control strategy are evaluated on an experimental setup as shown in Fig. 13. The experimental setup consists of two three-level T-NPC inverters feeding a dual three-phase 16 pole PMSM. The following capabilities of the proposed topology have been validated: 1) balancing DC-link voltages, 2) reduced output current distortion and 3) reducing capacitor RMS current.
Edge detection is widely used for detecting discontinuities in an image. Feature 7 is calculated in following way. The input face image is first converted
In our algorithm, we have already taken a good quality of image. 3) Binarize To binarize the image the ridges are denotted by black and furrow are denotted by the white.
In conclusion, “changes to objects that are central to the meaning of the scene or changes to visually distinctive objects are detected more readily than other changes, presumably because observers focus attention on important objects” (Rensink et al.,
Our lab results on all three data table experiments had a percent error less than 5 percent. When examining these results I can be almost certain it was not systematic error due to the fact that a major percent error was not detected on every trial that was run in each of the three tables. With there being some percent error there is the possibility for random error which are from unknown factors, which could come from impact of outside forces like the air track interfering with the acceleration of the cart. Beings that this was the first lab for my lab partners and I were working there was room for slight personal errors with our use of the computer program as well as the lab equipment.
we use a training set with front views, back views and side views of actors as shown in Figure
This selective prioritisation involves directing the person’s attention to a specific location in a particular space. When a space is picked, the information is further processed. Visual-spatial tasks are different compared to other forms of visual attention, which focus on an object and its entirety; the location of the object is not accounted for, whereas visual-spatial attention focuses solely on one region of space. There are two types of selection: early selection – which takes place at an early stage of perception; and late selection – selection that takes place at a late stage of
For the output layer, the input values oi, and the output values oo (also denoted by y) are given by equation 5.4 and 5.5. ξk in equation 5.4 is a threshold value which is also adapted during the training procedure. Equation 5.5 sigmoid transfer function is applied. oi_k=∑_(j=1)^(n hidden)▒ who_(j,k).ho_j+ξ_k (5.4) y_k=〖oo〗_k=1/(1+exp(-〖oi〗_k))
This sort of pattern matching decision making is excellent for many fields, including speech recognition, flight
This proposed model is shown in figure 2. These layers includes the pre processing stage, feature extraction stage and the classification
Huffman’s coding data from JPEGSnoop will be transferred to training set-all four columns are to given line by line which will create a vector. Examples of clear and coded inputs in a training set are in Figure 3 and Figure 4. {0,82,2811,886,837,724,547,213,44,0,0,0,0,0,0,0,0,537,494,602,542,475,293,112,17,0,0,0,0,0,0,0,0,111597,38817,46384,30163,5825,14139,6943,2526,2580,658,206,0,0,32,947,0,41239,30606,31571,18650,7639,724,3479,842,352,150,54,0,7,11,27} Figure 3: Example of clear input in training set {0,240,2734,853,811,715,535,212,44,0,0,0,0,0,0,0,0,534,497,603,542,474,293,112,17,0,0,0,0,0,0,0,0,111447,39851,46280,30122,5796,14067,6953,2498,2569,621,179,0,015,681,0,41366,30474,31522,18612,7645,716,3524,847,357,158,54,0,7,11,28} Figure 4: Example of coded input in training set As number show there is a difference but here are the examples of two pictures without and with secret messages inside (figure 5 and figure 6). For the first view there is no
Thinning: The boundary detection of image is done to enable easier subsequent detection of pertinent features and objects of interest.[6] II. IMAGE
The SVM is trained according to this labelled feature. The SVM kernel functions are used in the training process of
It is based on the gray level intensity value of pixels. Histogram of an image consists of valley and peaks where each peak represents the one region and the valley between the peaks represents the threshold value. On the basis of thresholding value, there are two types of threshold values such as global and local thresholding [18].The main drawback is that only two classes are generated