Neural Network based Situation
Detection and Service Provision in
the Environment of IoT
Source:2013 IEEE 78th , Vehicular Texchnology Conference
(VTC Fall)
Speaker:JIAN-MING,HONG
1
Outline
 Introduction
 Research back ground and related work
 Establishment of neural network
 Experiment preparations
 Conclusion
2
Introduction
 The safety production in coal mine has attracted
considerable research attentions due to the frequently
occurred mining accidents
 . In order to ensure the safety production in coal mine,
technology of the Internet of Things (IoT) is widely used
to detect the situation in coal mine.
3
Introduction
 To achieve the goal, numerous coal mine safety
monitoring systems have been deployed to provide
necessary service for the production
 According to the actual fact of coal mine situation and
the needs of this paper, we take the following situation
elements as our monitoring objects: the thickness of CO,
the thickness of CH4 (four places), temperature and
wind speed
4
Introduction
Sensor
network
Surveillance
component
Detection
component
5
Alarm
component
Introduction
6
Research back ground and related
work
 If few situation element changes or the changes are
distinct, this method will display good performance. But
it may be invalid when several situation elements change
simultaneously or one of the changes is ambiguous.
 After training, the neural network can identify the
various situations easily, it is very appropriate for
situation detection.
7
Research back ground and related
work
 we divide the situations into two categories. One is the
normal situation (situation 0, denote by S0), the other is
the dangerous situations, i.e., the emergency: (situation
1: S1; situation 2: S2; situation 3: S3).
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Research back ground and related
work
 S0: Safety situation. Denotes that the workspace is
safety and all the things in the coal mine are normal.
Alarm mode: non
Personnel notification: non
Disposal process: non
9
Research back ground and related
work
10
Establishment of neural network
 The course of from input layer to output layer, the
network adjusts the weights between layers from output
layer to input layer according to the principle of
reducing the errors.
11
Establishment of neural network
12
Establishment of neural network
13
Experiment preparations
 When the neural network has been trained for 7 times,
the mean square error between the output value and the
desired value is less than the target error.
14
Experiment preparations
15
Conclusion
 trained neural network can recognize ambiguous
situation. In future works, we will compare this method
with other situation detection methods and give the
experiment results. Additionally, we will continue to
research the situation detection and study the service
provision in the environment of the Internet of Things.
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