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positions will cause the swarm never to reach an equilibrium state While not all the methods discussed below consider this fact, all of the methods can address the problem by reducing the amount of chaos over time Kennedy and Eberhart [449] were the rst to mention the advantages of randomly reinitializing particles, a process referred to as craziness Although Kennedy mentioned the potential advantages of a craziness operator, no evaluation of such operators was given Since then, a number of researchers have proposed di erent approaches to implement a craziness operator for PSO When considering any method to add randomness to the swarm, a number of aspects need to be considered, including what should be randomized, when should randomization occur, how should it be done, and which members of the swarm will be a ected Additionally, thought should be given to what should be done with personal best positions of a ected particles These aspects are discussed next The diversity of the swarm can be increased by randomly initializing position vectors [534, 535, 863, 874, 875, 922, 923] and/or velocity vectors [765, 766, 922, 923, 924] By initializing positions, particles are physically relocated to a di erent, random position in the search space If velocity vectors are randomized and positions kept constant, particles retain their memory of their current and previous best solutions, but are forced to search in di erent random directions If a better solution is not found due to random initialization of the velocity vector of a particle, the particle will again be attracted towards its personal best position If position vectors are initialized, thought should be given to what should be done with personal best positions and velocity vectors Total reinitialization will have a particle s personal best also initialized to the new random position [534, 535, 923] This e ectively removes the particle s memory and prevents the particle from moving back towards its previously found best position (depending on when the global best position is updated) At the rst iteration after reinitialization the new particle is attracted only towards the previous global best position of the swarm Alternatively, reinitialized particles may retain their memory of previous best positions It should be noted that the latter may have less diversity than removing particle memories, since particles are immediately moving back towards their previous personal best positions It may, of course, happen that a new personal best position is found en route When positions are reinitialized, velocities are usually initialized to zero, to have a zero momentum at the rst iteration after reinitialization Alternatively, velocities can be initialized to small random values [923] Venter and Sobieszczanski-Sobieski [874, 875] initialize velocities to the cognitive component before reinitialization This ensures a momentum back towards the personal best position The next mportant question to consider is when to reinitialize If reinitialization happens too soon, the a ected particles may not have had su cient time to explore their current regions before being relocated If the time to reinitialization is too long, it may happen that all particles have already converged This is not really a problem, other than wasted computational time since no improvements are seen in this state Several approaches have been identi ed to decide when to reinitialize:.

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In computer aided drawing, a user creates shapes on a computer display We can create drawings in two distinct ways: using bitmapped graphics, as done in Windows Paint and Photoshop, or using object-oriented graphics, as done by Corel Draw and all CAD programs Bitmapped graphics are ideal for creating and storing pictures of great complexity, such as photographs and highly detailed images The reason for this is that each single dot, or pixel, that makes up a bitmapped picture is an object in its own right, with its own colour, and so however complex a photograph is, containing however many separate colours, it can be broken down into a grid of dots and represented as a bitmap The quality of a bitmap is determined by its resolution, which is related to the number of dots in each row and the number of rows of dots that make up the picture The limitation of a bitmapped image is that every picture contains a huge number of dots, but no matter how many dots there are if you move in close enough, you can see the individual dots An object-oriented or shape-oriented picture is made up of simple descriptions of each gure on it For example, a picture can be composed of a red circle centred on location 100, 100 with a radius of 50, and a blue rectangle whose top, left corner is at location 150, 400 and that is 200 units wide and 150 units high This type of picture is usually much simpler than a bitmapped photograph, but for some purposes, illustration and diagrams for example, it is preferable Pictures like this can be stored as simple descriptions and drawn as necessary, rather than stored as the colour of every single pixel in the picture They can also be more easily edited, so that a shape can be moved to a different location by changing a number or two, or deleted entirely without affecting the rest of the picture ( just try removing a person who strayed into one of your photographs in a program like Photoshop to see how dif cult this can be in a bitmapped image) Finally, we can redraw an object-oriented picture at any scale we like by changing the numbers that describe the shapes doubling each number would double the size of each shape as we draw it If we try to re-size a bitmapped image like this, each individual pixel must get bigger, and the picture quality reduces (Figure 915 shows how a bitmapped circle drawn at too large a scale would appear) A CAD drawing is stored as a list of objects, each of which contains numbers to specify its location in the picture, its size nd its colour Different shapes are.

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INTEGRATING COGNITION in Java Encode Universal Product Code ersion A in Java INTEGRATING COGNITION Scanner In Java Using Barcode reader for <Scene ID = 2> <Vision Time = 2/11/2005 12: 20:59/> <Novel> <Thing> <Chair> <Position> <Distance> 45 lt;Meters/> </Distance> </Position> <Color> Brown </Color> </Chair> <Floor> <Position> <Distance> 2 <Meters/> </Distance> </Position> </Floor> <Couch> <Position> <Distance> 3 <Meters/> </Distance> </Position> <Color> Brown </Color> </Novel> </Thing> <Unrecognized> <Blob> 3242 <Color> RGB </Blob> </Unrecognized> </Vision> <Speech Time = 2/11/2005 12:20:59> <Transcript> hi honey I m home </Transcript> <LCS> <Thing> honey </Thing> <State> I m </State> <Place> home </Place> </LCS> <Speaker ID = <User> Joe </User> </Speech> <Place> <Hypothesis> <home/> 025 </Hypothesis> </Place> </Scene> A ood of <Novel/> <Thing/>s of known types reinforces the hypothesis that the <Scene/> has changed, for a new <Scene ID/>.

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Although still computationally intensive, vision algorithms can detect, recognize, and track known types of objects in such visual scenes to yield a <Sketch/> of <Novel/> but recognizable new <Thing/>s As the <User/> moves through the scene, some previously unrecognized <Blob/>s may be recognized, causing the recognized <Thing/> to be asserted and the <Novel/> <Blob/> to be retracted As a new owner proceeds to different rooms of the house, new <Wall/>, <Picture/>, <Door/>, <Window/>, and related interior <Things/> and <People/> will be sensed Over time, with reinforcement and with <Sleep/> cycles, the AACR with evolved UKOs and UDHs for aggregating <Scenes/> repeatedly experienced over time may converge on <Home/> <Scenes/> populated with <Things/> and transited by <People/> and other animate entities like pets, some of which the AACR will assist with information access via typical conversations that reoccur in the scene.

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Awareness can be thought of as a process of mutually resolving noisy and con icting sensory perceptions across auditory, visual, and navigation sensors into a data structure that describes a very complex scene in terms computationally workable for use cases such as a LCS <Sketch/> Scenes and the microworlds that they describe vary over time, yet, the iCR must generate plans, make decisions, and take appropriate information access actions onRelated: Generate UPC-A Word , Intelligent Mail Generating ASPNET , VBNET Code 128 Generation.

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