projets:plim:20142015:gr6
Différences
Ci-dessous, les différences entre deux révisions de la page.
| Les deux révisions précédentesRévision précédenteProchaine révision | Révision précédente | ||
| projets:plim:20142015:gr6 [2014/11/23 21:22] – zayani | projets:plim:20142015:gr6 [2014/11/23 23:00] (Version actuelle) – zayani | ||
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| a. Battery optimization< | a. Battery optimization< | ||
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| c. Data Storage | c. Data Storage | ||
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| - | Our project is based on an MVC architecture with a model, controller and view, we have an GPS module with an asynchrone | + | Our project is based on MVC architecture with a model, controller and view for collecting data. We have a GPS module with an asynchronous |
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| - | Classification Module is designed like a service, it takes data as an input and return classified data. The classification is divided into two part, K-means and sequential | + | Classification Module is designed like a service; it takes data as an input and return classified data. The classification is divided into two parts; |
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| - | This classification is performed once every week, at the start of the app or on demand of the user.We do this to avoid generating | + | This classification is performed once every week, at the start of the app or on demand of the user. We do this to avoidgenerating |
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| - | Data are stored in a file in a JSON format, loaded at the start of the application | + | Data are stored in a file in a "JSON" |
| To save, we use <a href=" | To save, we use <a href=" | ||
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| we can define our architecture by this diagram:< | we can define our architecture by this diagram:< | ||
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| If the diagram is not correctly displayed, <a href=" | If the diagram is not correctly displayed, <a href=" | ||
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| - Supervised Classification< | - Supervised Classification< | ||
| - | The supervised classification methods are based on user-defined classes and corresponding representative sample sets. The sample sets are specified by training | + | The supervised classification methods are based on user-defined classes and corresponding representative sample sets. These sample sets are specified by training data sets, which must be created prior to entering the Automatic Classification process. The supervised Classification methods are: Minimum Distance to Mean, Maximum Likelihood, |
| - | The supervised Classification methods are: Minimum Distance to Mean, Maximum Likelihood,... | + | |
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| - Unsupervised Classification <br/> | - Unsupervised Classification <br/> | ||
| - | The unsupervised classification methods are algorithms that analyze and classify a large number of raster cells. | + | The unsupervised classification methods are algorithms that analyze and classify a large number of raster cells. |
| - | Some unsupervised classification | + | |
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| The k-means is an Unsupervised Classification. Using to find groups of similar or related objects and different from (or unrelated to) the objects in other groups. | The k-means is an Unsupervised Classification. Using to find groups of similar or related objects and different from (or unrelated to) the objects in other groups. | ||
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| - | k-means clustering is a method of classifying items into k groupsusing | + | k-means clustering is a method of classifying items into k groups using the following steps:< |
| - Each point is assigned to the cluster with the closest centroid< | - Each point is assigned to the cluster with the closest centroid< | ||
| - A centroid is "the center of mass of a geometric object of uniform density" | - A centroid is "the center of mass of a geometric object of uniform density" | ||
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| - public List$Slotime slotimeForOneday(List$Slotime brutSlotimes, | - public List$Slotime slotimeForOneday(List$Slotime brutSlotimes, | ||
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| - | After that, we will group similar SlotTimes and define their frequencies from a list of SlotTimes corresponding to a day of week | + | After that, we will group similar SlotTimes and define their frequencies from a list of SlotTimes corresponding to a day of week. |
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| In order to do this, the partition of frequencies is based on three parameters: Start Time, End Time and the Location. To simplify, we have chosen a margin of error for about 15 min. <br/> | In order to do this, the partition of frequencies is based on three parameters: Start Time, End Time and the Location. To simplify, we have chosen a margin of error for about 15 min. <br/> | ||
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| + | Download project from GoogleDrive</ | ||
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| The interface of our application " | The interface of our application " | ||
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projets/plim/20142015/gr6.1416777734.txt.gz · Dernière modification : 2014/11/23 21:22 de zayani