编辑-地球科学江南体育网页版堆栈交换江南电子竞技平台

你不是登录。你的编辑将被放置在一个队列,直到同行评议。

我们欢迎编辑,使读者更容易理解和更有价值。因为社区成员审查编辑,请尽量使文章更好比你怎么发现,例如,通过修正语法或添加额外的资源和超链接。

牧师

标题

身体

我目前从事的项目我的大学,遇到了麻烦。我对室内定位和构建应用程序现在试图基准的实现。为此我使用具有参考价值的数据集包含一个扫描点的经度和纬度,但我不知道,如何解释这样的坐标:纬度:4864889.6629166845经度:-7515.916799399859[这][1]是链接到数据表。也许你们有一些经验之一,可以帮助我,谢谢你的帮助!在评论——每个请求,这里有一些数据和解释以防<年代> < / s >链接时腐烂(他们总是做!):属性信息:属性001 (WAP001): WAP001强度值。负整数的值从-104到0,+ 100。正值100年如果没有检测到WAP001使用。....520属性(WAP520): WAP520强度值。负整数的值从-104到0,+ 100。如果没有检测到WAP520积极Vvalue 100使用。 Attribute 521 (Longitude): Longitude. Negative real values from -7695.9387549299299000 to -7299.786516730871000 Attribute 522 (Latitude): Latitude. Positive real values from 4864745.7450159714 to 4865017.3646842018. Attribute 523 (Floor): Altitude in floors inside the building. Integer values from 0 to 4. Attribute 524 (BuildingID): ID to identify the building. Measures were taken in three different buildings. Categorical integer values from 0 to 2. Attribute 525 (SpaceID): Internal ID number to identify the Space (office, corridor, classroom) where the capture was taken. Categorical integer values. Attribute 526 (RelativePosition): Relative position with respect to the Space (1 - Inside, 2 - Outside in Front of the door). Categorical integer values. Attribute 527 (UserID): User identifier (see below). Categorical integer values. Attribute 528 (PhoneID): Android device identifier (see below). Categorical integer values. Attribute 529 (Timestamp): UNIX Time when the capture was taken. Integer value. >Data Set Information: >Many real world applications need to know the localization of a user in the world to provide their services. Therefore, automatic user localization has been a hot research topic in the last years. Automatic user localization consists of estimating the position of the user (latitude, longitude and altitude) by using an electronic device, usually a mobile phone. Outdoor localization problem can be solved very accurately thanks to the inclusion of GPS sensors into the mobile devices. However, indoor localization is still an open problem mainly due to the loss of GPS signal in indoor environments. Although, there are some indoor positioning technologies and methodologies, this database is focused on WLAN fingerprint-based ones (also know as WiFi Fingerprinting). >Although there are many papers in the literature trying to solve the indoor localization problem using a WLAN fingerprint-based method, there still exists one important drawback in this field which is the lack of a common database for comparison purposes. So, UJIIndoorLoc database is presented to overcome this gap. We expect that the proposed database will become the reference database to compare different indoor localization methodologies based on WiFi fingerprinting. >The UJIIndoorLoc database covers three buildings of Universitat Jaume I with 4 or more floors and almost 110.000m2. It can be used for classification, e.g. actual building and floor identification, or regression, e.g. actual longitude and latitude estimation. It was created in 2013 by means of more than 20 different users and 25 Android devices. The database consists of 19937 training/reference records (trainingData.csv file) and 1111 validation/test records (validationData.csv file). >The 529 attributes contain the WiFi fingerprint, the coordinates where it was taken, and other useful information. >Each WiFi fingerprint can be characterized by the detected Wireless Access Points (WAPs) and the corresponding Received Signal Strength Intensity (RSSI). The intensity values are represented as negative integer values ranging -104dBm (extremely poor signal) to 0dbM. The positive value 100 is used to denote when a WAP was not detected. During the database creation, 520 different WAPs were detected. Thus, the WiFi fingerprint is composed by 520 intensity values. >Then the coordinates (latitude, longitude, floor) and Building ID are provided as the attributes to be predicted. >Additional information has been provided. >The particular space (offices, labs, etc.) and the relative position (inside/outside the space) where the capture was taken have been recorded. Outside means that the capture was taken in front of the door of the space. >Information about who (user), how (android device & version) and when (timestamp) WiFi capture was taken is also recorded. [1]: https://archive.ics.uci.edu/ml/datasets/ujiindoorloc#

标签

编辑总结

取消

\ begingroup美元我猜想应该是48.648866 N, 75.15916799 W。 \ endgroup美元
- - - - - -gerrit ♦
2020年2月25日19:59
\ begingroup美元我已经试过,但是jaume大学创建的数据集在西班牙(39.995015,-0.069154),应该至少纬度之间~ 36和~ 42。但谢谢你的努力! \ endgroup美元
- - - - - -Trup3s
2020年2月25日20:04
1

\ begingroup美元我猜是UTM米,但假设区31这些坐标点的纬度43 n,这是一些几百公里北大学Jaume我校园。然而,假设数据集将这些坐标米跨度只有几百米,有意义的校园小大学。然后,我猜更新以北和以东在米还来历不明。总之,我认为最好的建议是问作者,告诉我们完成。 \ endgroup美元
- - - - - -父亲
2020年2月25日20:44
3

\ begingroup美元远程链接,请复制粘贴的重要组成部分,使你的问题理解远程后下降。 \ endgroup美元
- - - - - -peterh
2020年2月25日23:36
1

\ begingroup美元可能是一个投影坐标系。应该是元数据的一部分 \ endgroup美元
- - - - - -f.thorpe ♦
2021年9月10日,3

| 显示1更多的评论

正确的小错误或错误
澄清的意思,而不更改它
添加相关资源或链接
总是尊重作者的意图
不使用编辑与作者回复